This is not a traditional analytics role. On this team, data scientists do the core statistical and modeling work, but they also build real tools and products: evaluation explorers, operator workflows, decision-support systems, experimentation surfaces, and customer-specific AI/data applications that get used in production.

The right candidate is strong in first-principles problem solving, rigorous measurement, and technical execution. They know how to define metrics, design experiments, diagnose failures, and build systems that people actually use. They are also comfortable using modern AI-assisted development tools to prototype and iterate quickly without sacrificing reliability, observability, or judgment. Python and SQL matter in this role, but as execution fluency in service of building better products and making better decisions.

Responsibilities: Partner directly with enterprise customers to understand workflows, operational pain points, constraints, and success criteria Turn ambiguous business and product problems into measurable solutions with clear metrics, technical designs, and deployment plans Design and build internal and customer-facing data products, including evaluation tools, workflow applications, decision-support systems, and thin product layers on top of data/ML systems Build end-to-end solutions across data ingestion, transformation, experimentation, statistical modeling, deployment, monitoring, and iteration Design evaluation frameworks, benchmarks, and feedback loops for ML/LLM systems, human-in-the-loop workflows, and model-assisted operations Apply rigorous statistical thinking to experimentation, causal inference, metric design, forecasting, segmentation, diagnostics, and performance measurement Use AI-assisted development workflows to accelerate prototyping and product iteration, while maintaining strong engineering discipline Diagnose failure modes across data quality, model behavior, retrieval, workflow design, and user experience, and drive fixes into production Act as the voice of the customer to Product, Engineering, and Data Science, using field learnings to shape roadmap and platform capabilities

Requirements: 5+ years of experience in data science, machine learning, quantitative engineering, or another highly analytical technical role Proven track record of shipping data, ML, or AI systems that delivered measurable business or product impact Exceptional ability to structure ambiguous problems, define the right success metrics, and translate them into executable technical plans Strong foundation in statistics, experimentation, causal reasoning, and measurement Experience building tools or products, not just analyses , for example internal workflow tools, evaluation systems, operator-facing products, experimentation platforms, or customer-specific applications Hands-on fluency in Python, SQL, and modern data/AI tooling; able to inspect data, prototype quickly, debug deeply, and productionize solutions that work Comfort using AI-assisted coding and development workflows to move from idea to usable product quickly Strong communication and stakeholder management skills; able to work effectively with customers, engineers, product teams, and executives High ownership and bias toward shipping in fast-moving environments with incomplete information

Preferred qualifications: Experience in a forward deployed, solutions, consulting, or other client-facing technical role Experience designing evaluation frameworks for LLMs, retrieval systems, agentic workflows, or other AI-enabled products Experience with large-scale data processing and distributed systems such as Spark, Ray, or Airflow Experience with cloud infrastructure and modern data platforms such as AWS, GCP, Snowflake, or BigQuery Experience building lightweight applications, APIs, internal tools, or workflow software on top of data/ML systems Familiarity with marketplace experimentation, causal inference, forecasting, optimization, or advanced statistical modeling Strong product instinct and the judgment to know when the right answer is a model, an experiment, a tool, or a workflow redesign

What success looks like: Success in this role means taking a messy, high-stakes customer problem and turning it into a deployed system that is actually used. Sometimes that system is a model. Sometimes it is an evaluation framework. Sometimes it is an operator-facing tool or a lightweight data product that changes how decisions get made. In all cases, success is defined by measurable impact, rigorous evaluation, and reliable execution.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Salary Range: $167,200-$209,000 USD

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Our team combines rigorous empirical methods with novel measurement approaches. We're building first-of-its-kind datasets tracking AI's impact on labor markets, productivity, and economic transformation. Using our privacy-preserving measurement system (Clio), we analyze millions of real-world AI interactions to understand how AI augments and automates work across different occupations and tasks.

Responsibilities:

• Make fundamental contributions to the development and expansion of the Anthropic Economic Index, including quarterly reports and industry-specific deep dives

• Design and conduct empirical research on AI's economic effects, drawing on external data sources and the privacy-preserving measurement systems internally

• Develop new methodological approaches for studying AI's impact on:

• Labor markets and the future of work

• Productivity and task transformation

• Economic inequality and displacement

• Industry-specific disruption and adaptation

• Aggregate economic trajectories (GDP, productivity, unemployment) under varying AI-adoption scenarios

• Develop causal-inference tooling , e.g. surrogate indexes, heterogeneous-effect pipelines , to help Anthropic evaluate the downstream economic consequences of its own compute, product, and pricing decisions

• Build and maintain relationships with academic institutions, policy think tanks, and other research partners

• Work cross-functionally with other technical teams to improve our measurement infrastructure and data collection

• Translate research insights into actionable recommendations for both product decisions and policy discussions

• Amplify external engagement through research publications, policy briefs, and presentations to diverse stakeholders

You May Be a Good Fit If You Have:

• PhD in Economics

• Strong track record of empirical research, particularly studies combining novel data sources and economic theory or those implementing frontier methods in causal inference and machine learning

• Experience relevant to the study of AI’s impact on the economy, including:

• Labor market analysis and occupational change

• Task-based approaches to technological transformation

• Large-scale data analysis and econometric methods

• Large language models for social science research

• Policy-relevant economic research

• Experimental and quasi-experimental methods for causal inference

• Macroeconomic modeling and time series forecasting

• Agent-based modeling or large-scale simulation

• Technical skills including:

• Proficiency in Python, R, SQL, or similar tools for large-scale data analysis

• Experience working with novel datasets and measurement systems

• Comfort learning new technical tools and frameworks

• Demonstrated ability to:

• Lead complex research projects from conception to publication

• Communicate technical findings to diverse audiences

• Build relationships across academic, policy, and industry communities

• Strong interest in ensuring AI development benefits humanity

• Comfort working with AI systems and ability to think critically about their capabilities and limitations

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As a Senior Data Scientist, you will sit in the heart of a cross-functional product team and help turn messy, real-world signals into clear decisions. You will make sure we are capturing the right data, designing experiments that tell us what is actually driving outcomes, and translating findings into recommendations that teams can act on quickly.

When the insight is stable and valuable, you will help operationalize it through predictive models that improve provider and patient experiences.

Key responsibilities include:

• Being the analytics partner inside the pod, working closely with Product, Engineering, Design, Ops, and Clinical stakeholders to define questions, metrics, guardrails, and decision rules.
• Running rigorous experiments, designing and analyzing A/B tests and quasi-experiments with clear hypotheses, power considerations, and pre-defined success criteria.
• Connecting behavior to strategy, using funnel, cohort, segmentation, and lifecycle analysis to understand how people and providers experience Headway, and where product changes will have the biggest impact.
• Using causal inference when experiments are not possible, applying approaches like diff-in-diff, matching, and regression-based designs with principled uncertainty quantification.
• Building models when they should exist, developing predictive models that operationalize vetted insights (feature development, validation, backtesting, calibration), with clear launch criteria and monitoring plans.
• Creating decision-ready work, producing analysis and narratives that are crisp, honest about uncertainty, and drive action.

To be successful in this role, you will need:

• 6+ years using data to drive product or business decisions in product, growth, engineering, or operations environments.
• Strong SQL and strong proficiency in Python or R for analysis and modeling.
• Demonstrated depth in experimentation and causal inference under real-world constraints.
• Practical modeling skill: feature engineering, model comparison, cross-validation or backtesting, calibration, and post-launch monitoring.
• Strong product sense and opinions, including a track record of connecting analytics recommendations to measurable outcomes.
• Clear communication: you can explain complex work to non-technical audiences without losing the truth.
• A self-starter mindset: you prioritize well, follow through, and do not need heavy oversight.
• Motivation for our mission: improving access and affordability in mental healthcare.

The expected base pay range for this position is $180,000 - $225,000, based on a variety of factors including qualifications, experience, and geographic location. In addition to base salary, this role may be eligible for an equity grant, depending on the position and level.

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Your work will set the default methods for how we answer questions like:

• Did this actually cause the outcome we care about?
• How sure are we, and what should we do given that uncertainty?
• What evidence is strong enough to change strategy, policies, or spend?

A major objective of this role is to build and institutionalize a clear map of “what we know” about patients, providers, and payers, with explicit confidence levels that tie directly to business action.

Responsibilities:

• Own causal inference and experimentation standards across Headway.
• Define the canonical approaches, guardrails, documentation, and review mechanisms for experiments and quasi-experiments, including when and how to use Bayesian methods.
• Build the confidence ladder for company knowledge.
• Create a clear, shared framework that maps findings to levels of confidence (for example 1–10), where lower levels reflect correlation and early directional evidence, mid levels reflect increasingly credible causal inference, and the highest levels reflect stable, repeatable, decision-grade truths.
• Operationalize it so it shows up in artifacts teams actually use: PRDs, launch reviews, growth planning, quarterly business reviews, and postmortems.
• Design the learning strategy for our hardest questions.
• Lead the approach for ambiguous, high impact domains like provider activation and retention, payer economics and policies, patient conversion and engagement, and marketplace dynamics.
• Recommend the right combination of randomized experiments, stepped rollouts, geo tests, natural experiments, and observational designs.
• Raise the organization’s statistical maturity.
• Introduce and standardize Bayesian experimentation practices where it improves speed and decision quality (priors, posterior interpretation, sequential decision rules, credible intervals, expected value framing).
• Build training, playbooks, and reusable tooling.
• Be the escalation point for difficult measurement problems.
• Tackle issues like interference and spillovers, network effects, selection bias, noncompliance, measurement error, multiple comparisons, seasonality, and Simpson’s paradox showing up in real life and causing confusion.
• Partner with Data Platform and Engineering to make rigor scalable.
• Ensure experimentation and inference are supported by instrumentation, logging, metric definitions, semantic layers, and monitoring.
• Help define the minimal foundations required for trustworthy learning.
• Build a culture of clear claims.
• Establish norms for separating facts, estimates, assumptions, and uncertainties.
• Make it easy for teams to say “we do not know yet” without losing momentum, and easy for leaders to understand what is safe to act on.
• Mentor and set the bar.
• Coach other data scientists and analytics leaders.
• Create review standards for causal work,
• Support hiring for methodological depth,
• Represent Headway’s measurement philosophy internally and externally when appropriate.

Requirements:

• 12+ years of experience applying causal inference, experimentation, and advanced statistics to real-world product, growth, or operational decisions (or equivalent depth demonstrated through scope and outcomes).
• Deep expertise in causal inference across randomized and observational settings, including practical strategy for when clean experiments are not possible.
• Deep expertise in Bayesian methods for experimentation and decision-making, and strong judgment about when Bayesian approaches outperform frequentist defaults and when they do not.
• Strong SQL and strong proficiency in Python or R, including building reusable analysis tools and improving team workflows.
• Track record of setting org-wide standards that materially improved decision quality and execution velocity.
• Executive-level communication and influence: you can drive alignment across Product, Growth, Ops, Finance, and Engineering.
• Comfort operating in ambiguity, and the ability to turn it into crisp frameworks, clear recommendations, and measurable outcomes.
• Motivation for our mission: improving access and affordability in mental healthcare.

Nice to have:

• Experience in marketplaces, healthcare, insurance, or other regulated and complex incentive systems.
• Experience with experimentation under interference and network effects.
• Experience building experimentation platforms, analysis libraries, or statistical tooling used broadly across an organization.
• Familiarity with causal graphs, uplift modeling, and decision theory framing (expected value, value of information).

Compensation and Benefits: The expected base pay range for this position is $249,600 - $312,000, based on a variety of factors including qualifications, experience, and geographic location. In addition to base salary, this role may be eligible for an equity grant, depending on the position and level. We are committed to offering a comprehensive and competitive total rewards package, including robust health and wellness benefits, retirement savings, and meaningful ownership opportunities through equity.

• Benefits offered include:
• Equity compensation
• Medical, Dental, and Vision coverage
• HSA / FSA
• 401K
• Work-from-Home Stipend
• Therapy Reimbursement
• 16-week parental leave for eligible employees
• Carrot Fertility annual reimbursement and membership
• 13 paid holidays each year as well as a Holiday Break during the week between December 25th and December 31st
• Flexible PTO
• Employee Assistance Program (EAP)
• Training and professional development

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As a Staff Data Scientist, Marketing Analytics, you will be the senior analytical and strategic leader who makes marketing performance legible, credible, and actionable. You will partner closely with Growth Marketing leadership and channel owners across paid, lifecycle, and organic, plus Finance, Product, and Engineering.

Your job is to help Headway answer the questions that matter:

• What is truly incremental?

• Where should we invest next?

• What is driving performance shifts?

• How do we scale what works without fooling ourselves?

You will build the frameworks, analyses, and modeling approaches that enable the marketing team to move faster with confidence. This is high-stakes decision support for a growth engine that needs to compound and certainly not a dashboard-only role or “just attribution” role.

Responsibilities:

• Own incrementality measurement across channels. Design and analyze geo tests, holdouts, lift tests, and quasi-experimental approaches when randomized tests are not feasible. Define clear guardrails, decision rules, and what “good” looks like.

• Build a marketing measurement system that leaders trust. Define canonical metrics (CAC, LTV, payback, conversion, retention, capacity-adjusted ROI), ensure definitions are consistent, and create a clear measurement narrative that aligns Marketing, Finance, and Product.

• Turn ambiguity into a plan. When performance changes, you will diagnose why, quantify contributing drivers, and recommend concrete actions. You will be the person who can say, “Here’s what moved, here’s why we believe it moved, and here’s what we do next.”

• Develop and evolve modeling approaches where they create leverage. Build practical models such as LTV and retention forecasting, cohort value prediction, causal uplift models for lifecycle, and marketing mix modeling when appropriate. Focus on models that survive contact with reality: calibration, backtesting, and decision usefulness.

• Partner with Engineering on the measurement plumbing. Improve event instrumentation, identity resolution assumptions, offline conversion integration, and data quality monitoring so measurement is robust. Advocate for minimal, decision-critical requirements that unlock reliable learning.

• Design learning loops that scale. Create repeatable experimentation and analysis templates for channel and creative testing, including measurement of message by audience by surface. Increase testing velocity without lowering the truth standard.

• Influence strategy, not just reporting. Bring an evidence-based point of view on channel allocation, growth constraints, saturation, diminishing returns, and the tradeoffs between short-term acquisition and long-term retention and care outcomes.

• Uplevel the team. Mentor analysts and data scientists working on growth, set quality standards, and help establish best practices across experimentation, causal inference, and forecasting.

What will make you successful:

• 10+ years using data science, analytics, and experimentation to drive decisions in marketing, growth, or marketplace environments (or equivalent scope and demonstrated impact).

• Deep expertise in causal inference and incrementality in real-world marketing systems: you know the failure modes (selection bias, channel cannibalization, platform noise, attribution myths) and how to design around them.

• Strong SQL plus strong proficiency in Python or R, with the ability to build reliable, reusable analytical workflows.

• Practical modeling skill, especially as applied to marketing and growth: cohorting, forecasting, LTV estimation, saturation and diminishing returns, MMM concepts, calibration and monitoring.

• Track record of influencing executive decisions with clear recommendations and measurable outcomes, not just analysis.

• Excellent communication: you can make complex measurement logic understandable and defensible to non-technical partners, and you can call out uncertainty without losing momentum.

• High ownership and strong judgment: you prioritize what changes decisions, you move quickly, and you know when to slow down because the risk is real.

• You are motivated by the mission. Access and affordability in mental healthcare are not abstract problems here.

Nice to have:

• Experience with geo experiments, marketplace constraints, or capacity-aware marketing optimization.

• Experience measuring acquisition quality beyond conversion: downstream engagement, retention, clinical matching quality, and unit economics.

• Familiarity with lifecycle marketing measurement (incrementality, uplift, experimentation design for messaging).

• Experience partnering with Finance on budget allocation, payback, and scenario planning.

• Comfort working with imperfect identity, privacy constraints, and evolving attribution ecosystems.

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This is not a reporting role. This role owns end-to-end data products - from problem framing and modeling to deployment and operational integration - that directly influence how our GTM organization prioritizes leads, manages accounts, allocates resources, and drives revenue.

You’ll work closely with Marketing, Sales, and RevOps leadership to build ML-powered systems that change how decisions are made at scale.

What will I be doing?

• Build Revenue-Impacting ML Systems
• Develop, deploy, optimize predictive models (lead scoring, account prioritization, marketing attribution, revenue estimation)
• Productionize models into operational systems (Salesforce, Marketo, outbound workflows)
• Monitor model performance and iterate for measurable business lift
• Design and implement experimentation frameworks (A/B testing, holdouts, incremental lift measurement)
• Apply advanced techniques when appropriate (e.g., causal inference, uplift modeling, segmentation, LTV modeling)

You don’t just build models - you ensure they change behavior.

Own End-to-End Data Products

• Translate ambiguous business problems into clear, measurable objectives
• Define GTM data products vision, success metrics, and roadmap
• Ensure integration into existing workflows and systems
• Lead stakeholder alignment and change management
• Secure buy-in from system owners before replacing or enhancing existing solutions

You operate as a mini GM for your data products.

Architect Scalable Data Foundations

• Design robust data pipelines and modeling infrastructure in collaboration with Data Engineering / Data Infrastructure
• Ensure data quality, governance, and reproducibility
• Elevate the team’s standards for experimentation, documentation, and knowledge sharing
• Push adoption of new tools and AI capabilities where appropriate

You raise the technical bar for the GTM organization.

What impact might I have?

Within 6-12 months, you might:

• Launch predictive models that materially improve conversion, expansion, or retention
• Reduce inefficiencies in Sales workflows through automation
• Help leadership make investment decisions backed by rigorous data science
• Influence GTM strategy through quantitative insight and modeling

Success is measured in business outcomes - not dashboards built.

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Responsibilities:

• Inspire, lead, and grow a team of data scientists to achieve our longer-term vision
• Drive data science projects end-to-end in partnership with Product, Engineering, and other partners to inform product strategy and investment decisions
• Analyze large datasets to identify trends, patterns, and insights that can help understand marketplace dynamics and help cross-functional teams (e.g., product, engineering, marketing) to define and execute data-driven optimization strategies
• Actively influence the design of the strategy and shaping of the roadmap. Generate and use team insights to set and prioritise longer-term goals
• Create and implement A/B testing, experimentation, and other cutting-edge statistical/mathematical frameworks to analyse Ads (marketplace) performance
• Continually develop & execute on a Data Science roadmap and vision for your team
• Stay abreast of industry best practices and emerging technologies in the field of advertising and data science
• Foster a culture of innovation, collaboration, and technical excellence
• Be an integral part of the Data Science Org, leveraging and contributing to the vibrant knowledge base, shared across a community of world-class data experts

Required Qualifications:

• PhD or Master's degree in Economics, Statistics, Machine Learning, Operations Research, or other quantitative fields
• 4+ years of management experience. Experience managing managers is a bonus
• Strong skills in programming (Python or R) and SQL
• Experience in leveraging AI-assisted development tools (e.g., Cursor, Claude, or similar LLMs) to improve personal and team productivity, code quality, and technical problem-solving
• Deep understanding of Ads Marketplace
• Extensive experience of online experimentation and causal inference
• Ability to communicate & discuss low-level technical topics as well as high-level strategies
• Comfortable in innovative and fast-paced environments, and an innate ability to bias toward action
• Results-oriented with a strong customer and business focus
• Track-record driving product roadmap and execution with strong cross-org collaborations
• Ability to provide structure and tackle ambiguous and undefined problems

Benefits:

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Comprehensive Medical Benefits & Health Care Spending Account
• Registered Retirement Savings Plan with matching contributions
• Income Replacement Programs
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

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Key Responsibilities:

• Team Leadership & Mentorship: Hire, lead, coach, and develop a high-performing team of product and marketing analysts.
• Analytics Strategy & Execution: Own the company's marketing measurement, experimentation, and insight generation for paid, owned, and integrated channels, and for performance across acquisition, activation, engagement, retention, and LTV.
• Hands-On Analysis: Perform deep-dive analyses to uncover insights that inform product and business decisions.
• Cross-Functional Collaboration: Act as a strategic thought partner to identify opportunities, measure success, and optimize Marketing and product performance.

Requirements:

• 10+ years of experience in analytics, with at least 2 years in a people management role.
• Strong technical skills in SQL, Python, data visualization tools (Amplitude, Tableau) and experimentation.
• Has direct experience in marketing analytics using Media Mix Models (MMM), difference-in-differences and other causal inference techniques.
• Proven track record of leading high-performing marketing or product analytics teams.
• Excellent communication and storytelling skills.
• Ability to balance strategic thinking with hands-on execution.

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As a Staff Data Scientist, you will be a key strategic partner to the Growth (Product and Engineering), Marketing, Sales, and Finance & Strategy teams, developing both intelligent data products and insights, and creating end-to-end systems and measurement plans for accelerating Stripe's overall growth engine.

Responsibilities:

• Provide direction to cross-functional partners on business strategy for enabling Stripe's growth, leveraging your expertise in causal inference / experimentation, modeling, analytical insights, and data foundations.
• Provide senior technical direction to data teams on horizontal technical areas, including experimentation, attribution, forecasting, observability, etc.; assume hands-on leadership, especially when helping teams resolve complex problems through iterative execution.
• Identify broad company problems and opportunities that can be tackled through data science; work with relevant teams to design and build the data science outputs that deliver outsized value to our users and our business.
• Contribute to the overall strategy, roadmap, and vision of your data science team and organization.
• Evangelize and inspire best practices across data science; lead by example to build a culture of craftsmanship and innovation.
• Provide mentorship to our data science talent to help them grow technically and professionally.

Minimum requirements:

• 10+ years of data science experience OR equivalent combined industry and research experience in a quantitative field.
• B.S. / M.S. / Ph.D. in a quantitative field (e.g. Statistics, Mathematics, Economics, Operations Research, Quantitative Marketing, Physical Sciences, Engineering, etc.).
• Experience with modern causal inference techniques.
• Demonstrated experience of leading organization-wide initiatives spanning multiple teams, or leveraging deep domain expertise to influence tech roadmap planning and execution.
• Demonstrated ability to effectively collaborate across multiple teams and stakeholders to drive business outcomes.
• Experience creating alignment with stakeholders in ambiguous and complex situations.
• Demonstrated ability to balance execution and velocity with research, statistical depth, and scalable design.
• Experience, mentoring, and investing in the development of peers.

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We're looking for a Data Scientist to join our team at Stripe, where you'll work closely with our Product, Finance, Payments, Security, Risk, Growth and Go-to-Market teams.

As a Data Scientist at Stripe, you'll play a crucial role in optimizing our systems and leveraging data to make strategic business decisions. You'll work with a variety of data science roles and teams across Stripe, and will be aligned to the most relevant team based on your background.

Responsibilities

• Work closely with a specific part of the business to optimize our systems and leverage data to make strategic business decisions
• Use techniques like machine learning, statistical modeling, causal inference, optimization, experimentation, and all forms of analytics to ensure that the company strategy, products, and user interactions make smart use of our rich data
• Partner deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly

Who you are

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum Requirements

• PhD + 3 years, MS/MA + 6 years or BS/BA 8 years of data science/quantitative modeling experience
• Proficiency in SQL and a computing language such as Python or R
• Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and/or experimentation
• Experience in working with cross-functional teams to deliver results
• Ability to communicate results clearly and a focus on driving impact
• A demonstrated ability to manage and deliver on multiple projects with a high attention to detail
• Solid business acumen and experience in synthesizing complex analyses into actionable recommendations
• A builder's mindset with a willingness to question assumptions and conventional wisdom

Preferred qualifications

• Experience deploying models in production and adjusting model thresholds to improve performance
• Experience designing, running, and analyzing complex experiments or leveraging causal inference designs
• Experience with distributed tools such as Spark, Hadoop, etc.
• A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)

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We're looking for a Data Scientist to join our team at Stripe. As a Data Scientist, you will work closely with our Product, Finance, Payments, Security, Risk, Growth and Go-to-Market teams to optimize our systems and leverage data to make strategic business decisions.

Responsibilities

• Partner with cross-functional teams to ensure that our users, products, and business have the models, data products, and insights needed to make decisions and grow responsibly.
• Analyze data, build machine learning and statistical models, and run experiments to drive impact.
• Influence how our products work, how our business works, and how our go-to-market motions operate.

Requirements

• PhD + 3 years, MS/MA + 6 years or BS/BA + 8 years of data science/quantitative modeling experience.
• Proficiency in SQL and a computing language such as Python or R.
• Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and/or experimentation.

Preferred qualifications

• Experience deploying models in production and adjusting model thresholds to improve performance.
• Experience designing, running, and analyzing complex experiments or leveraging causal inference designs.
• Experience with distributed tools such as Spark, Hadoop, etc.

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As a Staff Advanced Analyst, you will be a data thought partner to product and business leaders across teams through providing insights, recommendations, and enabling data-informed decisions. You will drive day-to-day analytics and create scalable data tools, identify pain points in travelling and hosting, and work with product leadership to improve experiences for our guest, host, and agent community.

In addition, you will leverage Airbnb's rich and unique data, state-of-the-art machine learning infrastructure, and other central data science tools to build and grow the measurement capacity within the organisation. You will also be deeply involved in the technical details of the various systems we build, and will have the opportunity to collaborate with a strong team of engineers, product managers, designers, and operations agents to achieve shared, cross-functional goals to help keep Airbnb's community safe and trusted.

Key responsibilities include:

• Leading and driving data-driven roadmaps for the CS Safety working groups
• Recommending actionable solutions backed by data and metrics to product and operational problems
• Building and owning an insights and reporting platform that measures and improves the effectiveness of behaviours, product interfaces, and processes across the CS Safety platform and contact centre network
• Performing data modelling of the various entities using tools and frameworks for optimising community and agent experiences
• Defining and evaluating key metrics in an unstructured problem space, including measurement of the ML models that drive product development
• Anticipating emerging safety risks through early-warning indicators, trend analysis, predictive modelling, and scenario planning to assess operational risk
• Influencing data-driven decisions across business verticals in day-to-day via business reviews, scorecards, self-serve portal, OKRs, and planning among others
• Influencing experimentation and measurement strategies; conducting power analyses, defining exit criteria, and using statistical models to improve inference

Requirements include:

• A minimum of 10+ years of industry experience in business analytics and a degree (Masters or PhD) in a quantitative field (e.g., Statistics, Econometrics, Computer Science, Engineering, Mathematics, Data Science, Operations Research)
• Experience supporting safety, risk, Trust & Safety, compliance, or employee wellbeing in high-volume call centre or customer operations environments
• Expert skills in SQL and expert in at least one programming language for data analysis (Python or R)
• Experience with non-experimental causal inference methods, experimentation, and machine learning techniques, ideally in a multi-sided platform setting
• Working knowledge of schema design and high-dimensional data modelling (ETL framework like Airflow)
• Ability to work under conditions of ambiguity in a fast-growth, sometimes uncertain and complex environment
• Comfortable operating independently with minimal planning, direction, and supervision
• Proven track record of influencing senior leaders and driving outcomes

Experience Level: Staff Employment Type: Full-time Workplace Type: Remote Category: Engineering Industry: Technology Salary Range: $176,000-$220,000 USD Required Skills: SQL, Python, R, Machine Learning, Data Analysis, Data Modelling, Causal Inference, Experimentation, Statistical Models Preferred Skills: Data Science, Operations Research, Statistics, Econometrics, Computer Science, Engineering, Mathematics

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Responsibilities:

• Develop action-oriented insights to drive the product strategy through observational causal analysis and experiment meta-analysis, and clearly communicate results to stakeholders up to the C-suite to take action based on the recommendations
• Uplevel experimentation practices on the team through guiding design, execution, and deep dive analyses to maximize learnings from A/B tests
• Create new ETLs, tables, dashboards, and other self-serve tools to enable other data scientists and cross-functional partners to find and interact with data seamlessly
• Design, evaluate, and/or measure team-level KPIs to enable quarterly goal setting and demonstrate team impact
• Regularly engage with stakeholders to gather feedback and share progress on work at all stages to ensure alignment between DS and other teams on business goals and outcomes
• Mentor more junior data scientists and business partners in data science best practices and methods to increase data literacy and improve decision making

Required Qualifications:

• Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research
• For M.S. holders: 5+ years of industry experience in applied science or data science roles
• For Ph.D. holders: 4+ years of industry experience in applied science or data science roles
• Expertise in querying relational databases (SQL) and programming languages (e.g., R / Python)
• Deep understanding of online experimentation (A/B testing), causal inference, and statistical techniques
• Comfortable in innovative and fast-paced environments, and a bias toward action
• Strong technical communication / data storytelling skills and demonstrated ability to discuss complex topics with technical and non-technical audiences alike
• Able to tackle ambiguous and undefined problems by creating an action plan, getting feedback, and iterating

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/. To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is $190,800-$267,100 USD

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Responsibilities:

• Design, develop, and apply DS solutions to inform improvements in advertiser experience and Reddit's ad platform
• Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform
• Collaborate with product managers and engineers to define product requirements and translate them into data science solutions
• Develop ML models & DS methods to improve anomaly detection, prediction, & pattern recognition
• Communicate findings and recommendations to stakeholders across the organization
• Stay up-to-date on the latest advancements in machine learning and data science
• Mentor and guide junior data scientists on the team

Qualifications:

• Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research
• For M.S. holders: 5+ years of industry experience in applied science or data science roles
• For Ph.D. holders: 4+ years of industry experience in applied science or data science roles
• Platform experience and a deep understanding of the ads ecosystem
• Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design
• Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus
• Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch
• Experience with SQL and relational databases
• Excellent communication and presentation skills

Bonus Points:

• Experience with online advertising and ad tech
• Experience with causal inference and A/B testing
• Contributions to open-source projects or publications in relevant conferences or journals

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

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This role is unique in that it directly improves the safety, trust, and quality of real-world user experiences by advancing Airbnb's ability to understand, interpret and act on content at scale. You will help shape how the platform reasons about listings, profiles, messages and other user-generated content by building the next generation of Trust Content Understanding Models.

The ideal candidate is a motivated and talented 'full-stack' Data Scientist with strong applied ML intuition and a bias toward impact, who can own and drive forward challenging, high-visibility initiatives such as:

• Advance Airbnb's content integrity capabilities by building Natural Language Processing (NLP) and LLM-based models that understand intent, policy compliance, quality and risk across listings, profiles, and user communications

• Develop high-performing models for detecting problematic or misleading content, including text classification, semantic similarity, information extraction and generative model-based reasoning for policy interpretation and enforcement

• Design and optimize human-in-the-loop Machine Learning (ML) systems for content review, labeling, escalation and continuous model improvement

• Build systems to detect emerging content risks and abuse patterns across regions, cohorts and surfaces using statistical, ML and representation-learning approaches

• Design intelligent sampling and evaluation strategies to measure rare events, policy recall, false positives/negatives and model blind spots in large-scale content systems

A Typical Day:

• Artificial Intelligence / Machine Learning: Build and deploy production AI/ML systems for content integrity and trust content understanding, including feature engineering, model development and evaluation, thresholding, error analysis and end-to-end model lifecycle management. This includes working with NLP and LLM-based models in real production settings.

• Inference: Partner with inference data scientists to conduct rigorous quantitative analyses, applying working knowledge of causal inference to interpret results, assess impact, and identify gaps and opportunities to improve content quality and trust outcomes.

• Optimization: Develop frameworks to analyze tradeoffs between enforcement accuracy, user experience, operational cost and coverage, and propose strategies to optimize overall system effectiveness.

• Communication & Collaboration: Deliver robust research reports with effective data visualizations, clear storytelling and bullet-proof accuracy to drive forward impact in collaboration with cross-functional partners in product, engineering and operations

• Empowerment: Think strategically about how to scale and evolve Airbnb's content integrity defenses, helping define the long-term vision for the role of AI-driven content understanding across the Trust ecosystem.

Your Expertise:

• 5+ years of industry experience in a quantitative analysis role with a Master’s degree in a quantitative field (computer science, statistics etc.), or 2+ years of experience with a Ph.D.

• State-of-the-art knowledge of AI/ML models

• Hands-on experience building, evaluating, and deploying NLP and LLM-based solutions, including text classification, information extraction, semantic understanding or generative applications.

• Working knowledge of causal inference

• Skilled in statistical programming (Python or R) and database usage (SQL)

• Proven ability to communicate clearly and effectively to audiences of varying technical levels

• Ability to translate complex findings and results into compelling narratives that drive impact

• Excellent project management, communication, and collaboration skills

• Trust & Safety experience is a plus

Your Location:

This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from.

Our Commitment To Inclusion & Belonging:

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply. We strive to also provide a disability inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: reasonableaccommodations@airbnb.com. Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process. We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application.

How We'll Take Care of You:

Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

Pay Range $177,000-$208,000 USD

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In this pivotal leadership role, you will define and implement the next generation of foundational data science solutions, leveraging expertise in statistics, econometrics, machine learning, and other quantitative methods to optimize Reddit's Ads marketplace.

As a thought leader, you will champion scientific rigor, causal inference, and economic modeling, while providing deep mentorship to the broader team.

Key responsibilities include:

• Defining the future of Ads Data Science and owning the design and long-term evolution of our core Ads Data Science solutions and infrastructure
• Identifying fundamental gaps and opportunities in our current systems and leading the strategic design and scientific roadmap for new solutions
• Taking end-to-end ownership of complex problem domains such as full funnel acceleration, advertiser lifetime value (LTV), and developing advanced predictive and causal frameworks
• Establishing scientific standards and defining best practices for large-scale statistical modeling, economic analysis, causal inference, offline model evaluation, and A/B experimentation

Requirements include:

• Demonstrated expertise in at least one of the following: ads marketplace understanding, auctioning/bidding, ads creative & format evaluation, measurement & experimentation at scale
• Master's or Ph.D. in Economics, Statistics, Computer Science, Operations Research, or a related quantitative discipline
• Advanced proficiency in statistical programming (Python or R) and SQL
• Experience with statistical analysis, economic modeling, foundational machine learning and/or optimization techniques
• Strong understanding of experimental design, causal inference, or A/B testing methodologies

Benefits include comprehensive healthcare benefits, income replacement programs, 401k with employer match, global benefit programs, family planning support, gender-affirming care, mental health & coaching benefits, flexible vacation & paid volunteer time off, and generous paid parental leave.

The base salary range for this position is $268,000-$365,100 USD.

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Responsibilities:

• Inspire, lead, and grow a team of data scientists to achieve our longer-term vision
• Drive data science projects end-to-end in partnership with Product, Engineering, and other partners to inform product strategy and investment decisions
• Analyze large datasets to identify trends, patterns, and insights that can help understand marketplace dynamics and help cross-functional teams (e.g., product, engineering, marketing) to define and execute data-driven optimization strategies
• Actively influence the design of the strategy and shaping of the roadmap. Generate and use team insights to set and prioritise longer-term goals
• Create and implement A/B testing, experimentation, and other cutting-edge statistical/mathematical frameworks to analyse Ads (marketplace) performance
• Continually develop & execute on a Data Science roadmap and vision for your team
• Stay abreast of industry best practices and emerging technologies in the field of advertising and data science
• Foster a culture of innovation, collaboration, and technical excellence
• Be an integral part of the Data Science Org, leveraging and contributing to the vibrant knowledge base, shared across a community of world-class data experts

Required Qualifications:

• PhD or Master's degree in Economics, Statistics, Machine Learning, Operations Research, or other quantitative fields
• 4+ years of management experience. Experience managing managers is a bonus
• Strong skills in programming (Python or R) and SQL
• Experience in leveraging AI-assisted development tools (e.g., Cursor, Claude, or similar LLMs) to improve personal and team productivity, code quality, and technical problem-solving
• Deep understanding of Ads Marketplace
• Extensive experience of online experimentation and causal inference
• Ability to communicate & discuss low-level technical topics as well as high-level strategies
• Comfortable in innovative and fast-paced environments, and an innate ability to bias toward action
• Results-oriented with a strong customer and business focus
• Track-record driving product roadmap and execution with strong cross-org collaborations
• Ability to provide structure and tackle ambiguous and undefined problems

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

XML job scraping automation by YubHub

Responsibilities:

• Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform
• Develop ML models & DS methods to for improved anomaly detection, prediction, pattern recognition
• Communicate findings and recommendations to stakeholders across the organization
• Collaborate with product, engineering, sales, and marketing partners to define product and program requirements and translate them into data science solutions
• Stay up-to-date on the latest advancements in machine learning and data science

Qualifications:

• Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research
• For M.S. holders: 3+ years of industry experience in applied science or data science roles
• For Ph.D. holders: 2+ years of industry experience in applied science or data science roles
• Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design
• Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus
• Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch
• Experience with SQL and relational databases
• Excellent communication and presentation skills

Bonus Points:

• Experience with online advertising and ad tech
• Experience with causal inference and A/B testing
• Contributions to open-source projects or publications in relevant conferences or journals

Benefits:

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Comprehensive Medical Benefits & Health Care Spending Account
• Registered Retirement Savings Plan with matching contributions
• Income Replacement Programs
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

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Our team combines rigorous empirical methods with novel measurement approaches. We're building first-of-its-kind datasets tracking AI's impact on labor markets, productivity, and economic transformation. Using our privacy-preserving measurement system (Clio), we analyze millions of real-world AI interactions to understand how AI augments and automates work across different occupations and tasks.

The ideal candidate will be comfortable working at the intersection of empirical economics, technological change, and policy impact. They will have a strong track record of empirical research, particularly studies combining novel data sources and economic theory or those implementing frontier methods in causal inference and machine learning.

Some examples of our recent work include:

• Anthropic Economic Index Report: Economic Primitives
• Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption
• Estimating AI productivity gains from Claude conversations
• The Anthropic Economic Index

For this role, we're looking for candidates who can combine rigorous economic analysis with novel measurement approaches to understand AI's transformative effects on the economy.

XML job scraping automation by YubHub

As a Data Science Manager at Reddit, you will collaborate closely with cross-functional partners to help us build and improve the systems that continuously drive our user growth. You will be responsible for driving the adoption of strategic and tactical recommendations based on deep experience with Reddit products and data skills.

Key responsibilities include:

• Serving as a thought-partner for product managers, engineering managers, and leadership to communicate and shape the roadmap and strategy for Reddit.
• Being proactively involved in all phases of product development, including ideation, exploratory analysis, opportunity sizing, metrics design, offline modeling, experimentation, and decision-making.
• Having a keen interest in the collection and quality of underlying data, including experiment design and analysis, data deep dive, ETLs, reporting dashboards, and data aggregations needed for business tracking and/or ML model development.

Requirements include:

• An advanced degree in a quantitative field such as statistics, mathematics, physics, economics, or operations research.
• 10+ years of industry experience for masters holders, or 6+ years for PhD holders.
• Experience in driving product strategy and roadmaps through analytics, ideally for consumer technology products and marketplaces.
• Leadership experience as a people manager, leading a team of 5+ data scientists.
• Expertise in causal inference, A/B testing experimentation, metric definition and governance, and product strategy.
• Proficiency in SQL and Python/R for hands-on analysis.

Benefits include comprehensive healthcare benefits, income replacement programs, 401k with employer match, global benefit programs, family planning support, gender-affirming care, mental health and coaching benefits, flexible vacation and paid volunteer time off, and generous paid parental leave.

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A typical day involves working closely with cross-functional stakeholders to define product scopes, evaluating impact, and setting roadmap priorities. You will architect and implement rigorous measurement plans, using A/B tests and quasi-experimental methods to assess product success and inform strategic bets. Additionally, you will interpret unexpected outcomes, identify bias in experiments, and drive solutions to ensure measurement quality.

Your expertise in causal inference and experimentation will be crucial in developing scalable frameworks, models, and systems that enable product features to be more refined and tailored to the needs of local markets. You will actively present data findings and ideas to stakeholders at different levels, turning proposals into actions and tangible results for the business and our customers.

Key responsibilities include:

• Working closely with cross-functional stakeholders to define product scopes, evaluate impact, and set roadmap priorities
• Architecting and implementing rigorous measurement plans, using A/B tests and quasi-experimental methods to assess product success and inform strategic bets
• Interpreting unexpected outcomes, identifying bias in experiments, and driving solutions to ensure measurement quality
• Conducting in-depth research into customer behaviors and preferences across markets to unlock opportunities for international business growth
• Developing scalable frameworks, models, and systems that enable product features to be more refined and tailored to the needs of local markets

Requirements include:

• 5+ years of industry experience in a fast-paced tech environment with a BS/Master's in a technical field related to mathematics, computer science, statistics, economics, machine learning, or 2+ years of relevant experience and a PhD in similar fields
• Strong knowledge of causal inference and experimentation
• Expertise in SQL, Python, or R
• Ability to solve business problems using appropriate methods and models
• Strong stakeholder communication skills and the ability to translate complex analyses into compelling narratives and business actions

This position is remote eligible, with occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager.

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Our vision is simple: data drives every Databricks decision and action. To get there, we need a world-class statistician and communicator , someone who can bridge the gap between deep analytical rigor and executive decision-making. This is a pure IC role with company-wide influence: no direct reports, maximum leverage.

As a Principal Data Scientist, you will:

• Translate complex data science findings into clear, actionable narratives for the CEO, C-suite, and Board of Directors , ensuring data science insights directly inform the company's most critical decisions.
• Serve as the company's chief statistical voice and the final quality backstop for analytical rigor in high-stakes executive decisions. Advance the state-of-the-art in how Databricks applies statistical methods to business problems.
• Raise the communication bar across the entire Data Science organization by setting standards, coaching teams, and co-authoring key executive-facing deliverables. Make every DS team better at telling their story.
• Produce deep strategic analyses on revenue, platform health, operational efficiency, and competitive positioning , the kind of synthesized, judgment-rich insight that AI cannot autonomously create.
• Partner with engineering VPs, product leaders, and executive staff to embed a data-driven decision-making culture across the company. Be the trusted analytical advisor in rooms where critical decisions are made.
• Represent Databricks externally as a data science thought leader at industry conferences, in publications, and in the broader statistical community. Build an external identity that attracts world-class talent.
• Define and evolve company-wide scientific methodologies , experimentation frameworks, forecasting systems, causal inference approaches , to match and push industry state-of-the-art.

To be successful in this role, you will need:

• 15+ years of experience in data science, statistics, or quantitative research spanning industry and/or academia.
• Proven track record of presenting statistical and data science concepts to C-suite and Board-level audiences, with measurable impact on executive decision-making.
• Broad expertise across data science disciplines: experimentation, causal inference, forecasting, optimization, and machine learning.
• Exceptional written and verbal communication , the ability to make complex statistical concepts intuitive and compelling for non-technical executives.
• Track record of upleveling teams: setting analytical standards, mentoring senior data scientists, and improving org-wide output quality.
• Experience at the intersection of statistics and large-scale technology or data platforms.
• Ph.D. in Statistics, Mathematics, Computer Science, or a related quantitative field.

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In this role, you'll apply strong analytical judgment and product intuition to help us understand customer behaviour, evaluate trade-offs, and make smart investment decisions across the cards and lending lifecycles , from eligibility and activation to spend, retention, incentives, and credit performance. You'll help build a data-informed culture across Mercury so teams can move quickly, measure what matters, and invest intelligently.

Key responsibilities include:

• Bringing impeccable communication and complete ownership , independently identifying opportunities, developing strong points of view, and influencing executives, Cards & Credit leaders, and cross-functional partners through clear, concise, and persuasive storytelling.
• Developing a nuanced understanding of cardholder behaviour and economics, helping teams reason about trade-offs between growth, engagement, risk, and unit economics.
• Defining, owning, and analysing metrics that inform both tactical decisions and long-term strategy across the cards and credit lifecycle (e.g., eligibility, activation, spend, utilisation, rewards, retention, loss signals).
• Designing and evaluating experiments using rigorous statistical approaches, including A/B testing, cohort analysis, causal inference techniques, and trend analysis.
• Building and improving data pipelines and tools to streamline data collection, processing, and analysis workflows, ensuring the integrity, reliability, and security of data assets.
• Building and deploying predictive models to forecast key outcomes, inform product treatments, and deepen understanding of causal drivers.

Requirements include:

• 7+ years of experience working with large datasets to drive product or business impact in data science or analytics roles.
• Fluency in SQL and comfort with Python.
• Strong judgment in defining and analysing product metrics, running experiments, and translating ambiguous questions into structured analyses.
• Exceptional proactivity and independence , identifying opportunities, forming strong points of view, and making your case to stakeholders.
• Experience with ETL processes and modern data modelling (e.g., dbt, dimensional models, Airflow), with a solid understanding of how data is produced and consumed.
• Experience in analytical approaches ranging from behavioural modelling to experimentation to optimisation , and, importantly, know when simpler approaches are the right answer.
• Apply AI tools to accelerate analytical and business workflows, improving scalability, decision quality, and reducing manual or repetitive work across teams.

Nice to have:

• Experience working on cards or credit products, with familiarity in card economics and lifecycle concepts (e.g., spend behaviour, interchange, rewards and incentives, utilisation, credit limits, retention).
• Experience developing quantitative pricing models or engines (e.g., dynamic pricing, incentive optimisation, or marketplace pricing systems).
• Experience applying optimisation techniques to resource allocation or decision systems (e.g., customer operations, capacity planning, or policy optimisation).
• Experience building or supporting credit models, including probability of default modelling, cashflow modelling, or dynamic credit limit setting.

XML job scraping automation by YubHub

In this role, you'll define how we measure performance, prioritize investments, and accelerate value creation across the customer lifecycle , with a high bar for rigor and a bias toward execution. You'll partner closely with Product, Engineering, Marketing, Sales, and Finance to ensure our most important decisions are grounded in trusted data and clear experimentation frameworks.

Key responsibilities include:

• Lead and develop a team of Data Scientists embedded across go-to-market, growth product, monetization, and core product experiences
• Define the measurement and experimentation strategy across the customer lifecycle , from acquisition and activation to monetization, expansion, and retention , ensuring our investments are grounded in rigorous analysis and trusted data
• Elevate the craft of experimentation, pricing and monetization analytics, and commercial performance measurement, balancing analytical precision with decision velocity
• Partner closely with Product, Marketing, Sales, and Finance to shape roadmaps, evaluate ROI, and guide revenue forecasting and capital allocation decisions
• Translate complex quantitative signals into clear insights that influence product direction and revenue strategy
• Increase team leverage by building scalable analytics systems and self-serve capabilities that power reliable, AI-enabled insights across growth, monetization, and core product experiences
• Operate effectively in ambiguity, setting clear priorities that balance user value, growth, and long-term business impact

Requirements include:

• 10+ years of experience, with 3+ years leading high-performing data teams
• Deep experience in growth, monetization, and product analytics, along with a proven track record of partnering with Product, Marketing, Sales, and Finance to shape roadmaps and drive revenue outcomes
• Strong business judgment, with the ability to balance analytical rigor with decision velocity in high-impact commercial environments
• Highly fluent in experimentation design, attribution, and causal inference, with the ability to raise the bar on analytical craft across a team of senior ICs
• Experience building scalable analytics frameworks and self-serve capabilities that increase leverage and support AI-enabled insight generation within growth and product domains
• Thrive in ambiguity, setting clear priorities that balance user value, growth, and long-term business impact

Total rewards package includes base salary, equity (stock options), and benefits. Salary and equity ranges are highly competitive within the SaaS and fintech industry.

XML job scraping automation by YubHub

Your work will ensure that every feature we launch is grounded in data-driven insight and meaningfully strengthens the connection between more than 700 million listeners and the creators they love.

Responsibilities:

• Own analytical projects end-to-end, from hypothesis generation and data exploration to recommendations for product leadership
• Develop and own success metrics for generative music features and systems
• Design and analyze A/B tests and causal studies to evaluate product and model impact
• Perform exploratory analyses to uncover opportunities that improve experiences for listeners and artists
• Build scalable dashboards to monitor feature health and ecosystem impact
• Design and run evaluations for generative music systems, assessing risks and opportunities across prompts, outputs, and quality
• Collaborate with Product, Design, Research, Marketing, and Engineering to translate insights into product requirements
• Partner closely with Engineers and AI Researchers to integrate evaluation signals into model development workflows
• Communicate complex findings through clear, actionable narratives that inform product strategy and roadmap decisions

Who You Are:

• You have a degree in Computer Science, Statistics, Economics, Operations Research, quantitative social science, or a related field (or equivalent experience)
• You bring 4+ years of experience as a Data Scientist influencing product decisions through data
• You're highly proficient in SQL and Python and comfortable working with large-scale datasets
• You use AI-powered tools (e.g., Cursor, Copilot) to accelerate analysis and workflows
• You have strong product intuition and a results-focused perspective, you seek the 'why' behind the data
• You have experience with A/B testing, causal inference and advanced statistical methods, and exercise strong judgment in methodological choices
• You understand machine learning systems and can evaluate models beyond offline metrics, applying human judgment to quality and impact
• You thrive in ambiguous, zero-to-one environments and enjoy defining metrics & opportunities for entirely new product categories
• You're motivated by creating real value for music fans and music creators

Where You'll Be:

• We offer you the flexibility to work where you work best! For this role, you can be within the EST timezone region as long as we have a work location.
• This team operates within the Eastern Standard time zone for collaboration

Additional Information: The United States base range for this position is $110,018 - $157,169 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave.

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Responsibilities:

• Apply machine learning, causal inference, or advanced analytics on large datasets to measure results and outcomes, identify causal impact and attribution, and predict the future performance of users or products to drive business success.
• Influence business actions and strategy by developing actionable insights through metrics and dashboards.
• Drive the collection of new data and the refinement of existing data sources.
• Learn quickly by asking great questions, finding how to work with your mentor and teammates effectively, and communicating the status of your work clearly.
• Present your work to the Data Science team, partner teams, and fellow interns.

Requirements:

• Enrolled in a quantitative Master's degree program (e.g. Data Analytics, Statistics, Economics, Mathematics, etc.) with the expectation of graduating in winter 2026 or spring/summer 2027.
• Experience with a scientific computing language (such as Python, R, etc) and SQL.
• Experience communicating and collaborating with multidisciplinary stakeholders in a team environment.

Preferred Qualifications:

• Experience writing and debugging data pipelines.
• Demonstrated ability to evaluate and receive feedback from mentors, peers, and stakeholders via experience from previous internships or other multi-person projects.
• Ability to learn new systems and form an understanding of those systems, through independent research and working with a mentor and subject matter experts.

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Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Strategic Finance

Compensation

• $230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

Benefits

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

The Strategic Finance team is responsible for some of the largest investment categories at OpenAI. We play a critical role in maximizing OpenAI’s long-term value by partnering across the business to allocate and deploy resources for the highest-impact outcomes. We work cross-functionally with Finance, Product, Engineering, and Go-To-Market teams to deliver actionable insights and support strategic decision-making.

About the Role

As one of the founding Data Scientists on the Strategic Finance Data Science team, you will design, implement, and own critical models, reporting, and analysis. You will work closely with Finance leadership, as well as teams across Research, Applied and Go-To-Market.

This role requires deep expertise in predictive modeling, causal inference, and the ability to surface insights to support high-stakes decision-making.

This role is based in our San Francisco HQ. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Become an expert in OpenAI’s data and systems. Partnering with Data Engineering, Finance, and other teams, you’ll self-serve data to derive critical insights.

• Scale causal inference models to measure the impact of different actions on user growth and financial outcomes.

• Architect customer segmentation frameworks to unlock tailored growth strategies and expand lifetime value.

• Automate standardized reporting and analysis by building data products or agentic solutions.

• Support product pricing and other business decisions with ad hoc analysis.

• Design and deliver dashboards for leadership to track key performance indicators and enable optimal decision-making.

• Manage and lead cross-functional data science projects end-to-end.

You might thrive in this role if you have:

• MS or PhD in a quantitative field, with 7+ years of experience in a highly quantitative role, ideally as an early data scientist at a hyper-growth company or research organization.

• Proficiency in Python, SQL, Tableau, and other analytical tools.

• Strong analytical skills and experience working with large, complex data sets.

• Proven ability to distill complex data into actionable insights for leadership.

• Ability to independently initiate and complete projects, owning them end-to-end.

• Excellent communication and storytelling skills when presenting data insights.

• Exceptional attention to detail and a commitment to accuracy.

You could be an especially great fit if:

• You’re an enthusiastic self-starter who thrives in fast-paced, dynamic environments.

• You’re a phenomenal teammate and communicator, able to explain complex topics with clarity and keep stakeholders proactively informed.

• You’re passionate about technology and artificial intelligence.

• You use first-principles thinking to design solutions from the ground up based on user needs and operational realities.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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Location

San Francisco

Employment Type

Full time

Department

Intelligence & Investigations

Compensation

• $230K – $325K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

Benefits

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

The Intelligence and Investigations team seeks to rapidly identify and mitigate abuse and strategic risks to ensure a safe online ecosystem. We are dedicated to identifying emerging abuse trends, analyzing risks, and working with our internal and external partners to implement effective mitigation strategies to protect against misuse. Our efforts contribute to OpenAI's overarching goal of developing AI that benefits humanity.

About the Role

As a Data Scientist, you will lead econometric and experimental analysis to understand how risk changes in complex human–AI systems. Your work will focus on measuring the magnitude and impact of risk shifts in a fast-paced, rapidly evolving operational environment. You will design experiments and observational studies to identify causal drivers and analyze changes in risk across a wide range of surfaces and sources. Your analyses will directly inform prioritization and strategic risk management across the company.

In this role, you will:

• Own the design and execution of experimental and observational analyses used to assess strategic risk

• Develop econometric approaches to estimate the impact of product, policy, and external developments on key risk vectors

• Translate strategic risk questions into testable hypotheses and sound study designs

• Design and deploy A/B tests, as well as pseudo-experimental studies, to measure changes in risks and understand underlying mechanisms

• Identify, test, and explain product-driven, event-driven, or signal-driven changes in risk

• Establish baselines and statistical confidence around core metrics to size these problems

• Partner across teams to track strategic risks, identify opportunities for intervention, and develop analyses to evaluate those interventions

You might thrive in this role if you:

• Have 3–6+ years in econometrics, causal inference, or experimental research

• Are comfortable owning ambiguous analyses with large-scale influence

• Are strong in experimental design, observational methods, and statistical reasoning

• Write solid Python and SQL

• Experience delivering zero-to-one analyses and scaling them from concept through deployment

• Communicate data-driven findings clearly, including uncertainty and trade-offs, to non-technical partners and leadership

• Nice to have: experience in trust and safety, integrity, operational security, intelligence analysis or other quantitative risk-focused domains

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

XML job scraping automation by YubHub

Location

San Francisco

Employment Type

Full time

Department

Strategic Finance

Compensation

• $230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Role This role focuses on building the strategic unit economics understanding of OpenAI, guiding sustainable growth to make it the most impactful company of our generation and beyond.

You will lead the development of foundational causal inference and data science models and frameworks to predict and quantify the drivers of customer lifetime value (LTV), translating deep data insights into strategic decisions and growth levers. The role requires both technical depth and executive-level communication.

This position is based in our San Francisco HQ with a hybrid work model (three days in office per week). Relocation assistance is available.

The Vision

• Build causal inference and predictive analytics capabilities to measure and forecast LTV across customer segments (B2C and B2B), and quantify the incremental impact of different actions or product features on customer LTV.

• Design customer “happy paths” by identifying adoption journeys that maximize lifetime value while ensuring customers gain the most from our ecosystem.

• Analyze price elasticity to guide product packaging, monetization, and pricing strategies.

In this role, you will:

• Partner with cross-functional teams (Finance, Product, Data Engineering, GTM, and other DS teams) to build causal inference and predictive models that drive business decisions.

• Develop and maintain LTV models across product lines and customer cohorts.

• Architect scalable frameworks and models that democratize economic insights for leadership and functional teams.

• Support strategic pricing and investment decisions with robust analytical and causal evidence.

• Lead cross-functional data science initiatives, ensuring analytical rigor, clarity, and timely delivery.

You might thrive in this role if you:

• Executive communication — ability to distill complex analysis into clear, actionable recommendations for leadership.

• Technical breadth — comfort spanning ROI analysis, causal inference, statistical modeling, and ML predictive models; strong experience with Python and SQL.

• Strategic judgment — ability to connect analytical insights to business impact, delivering the “so what” that informs leadership decisions.

• Collaboration and ownership — thrive in a fast-paced, cross-functional environment and proactively take projects from concept to delivery.

Qualifications

• MS or PhD in a quantitative field (Statistics, Economics, Applied Math, Operations Research, Computer Science, etc.).

• 7+ years of experience in applied data science, causal inference, or quantitative strategy.

• Proven record of delivering high-impact insights to executive leadership.

• Experience building scalable analytical frameworks and models that inform business decision-making.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

XML job scraping automation by YubHub

About the Team

OpenAI’s Financial Engineering (FinEng) team powers how revenue flows through our products—pricing & packaging, checkout, payments, subscriptions, and the financial infrastructure behind them. We partner with Product, Engineering, Risk, Finance, and Go-to-Market to make paying for OpenAI products seamless, reliable, and efficient worldwide.

About the Role

As a Data Scientist on FinEng, you’ll own the analytics and experimentation that improve our checkout and payments, subscriptions, and pricing & monetization systems. You’ll define the metrics that matter, build the source-of-truth data assets, and design experiments that increase conversion, reduce churn and payment failures, and expand global payment method coverage. Your work will directly influence revenue, customer experience, and how we scale internationally.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will

• Own checkout & payments analytics and experimentation across methods and locales (e.g., bank transfers, emerging rails), improving conversion while monitoring risk and latency.

• Build and run the experimentation program for in-house checkout—define success metrics and guardrails, execute staged rollouts, and use offline incrementality when online tests aren’t feasible.

• Create operational visibility and source-of-truth data with FinEng Data Engineering—land team-level metrics, SLAs, and self-serve dashboards that drive proactive action.

• Lead subscription, retention, and monetization analytics—ship launch-readiness for new subscription features, reduce involuntary churn (e.g., targeted retrials/nudges), and develop elasticity/FX frameworks toward pricing optimality.

You might thrive in this role if you have

• 5+ years in a quantitative role (data science, product analytics, or experimentation) in high-growth or fintech environments

• Fluency in SQL and Python, with a track record designing and interpreting A/B tests and quasi-experiments

• Experience building product metrics from scratch and operationalizing them for decision-making

• Excellent communication skills with PMs, engineers, risk/finance partners, and executives

• Strategic instincts beyond significance tests—clear thinking about tradeoffs (conversion vs. risk vs. cost vs. user experience)

You could be an especially great fit if you have

• Payments, checkout, or subscription analytics experience (PSPs, bank rails, disputes/refunds, risk, e-commerce)

• Background in offline incrementality methods, uplift modeling, CUPED/causal inference, or counterfactual evaluation

• Experience with internationalization/local payments, FX, and pricing & packaging strategy

• Comfort building operational analytics (alerting, SLIs/SLOs) and partnering closely with data engineering

Benefits

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick and safe time (1 hour per 30 hours worked)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

Salary

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus for eligible employees and benefits.

XML job scraping automation by YubHub

Location

San Francisco

Employment Type

Full time

Department

Data Science

Compensation

• $293K – $515K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

Benefits

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

OpenAI’s mission is to ensure AGI benefits all of humanity. The API organization is one of the highest-leverage ways we do that: we put frontier intelligence in the hands of builders who turn it into products, businesses, and services that reach people everywhere.

About the Role

As a Data Scientist on the API team, you’ll build the measurement systems that make our platform legible and improveable. You’ll define the metrics that matter, identify and quantify developer friction, evaluate launches and platform changes, and translate data into product decisions that improve reliability and developer outcomes at scale.

Responsibilities

• Own the core KPI framework for the API platform, spanning developer adoption, engagement, retention, and platform health.

• Build end-to-end funnels that identify where developers succeed or get stuck—from first integration through scaling to production.

• Define and operationalize platform guardrails (e.g., reliability, latency, error rates, cost/efficiency) and connect them to user outcomes.

• Design and evaluate experiments and rollouts to quantify the impact of platform and product changes.

• Partner with product and engineering teams to improve instrumentation, data quality, and metric definitions so decisions are fast and correct.

• Translate complex analysis into clear, actionable insights for leadership and cross-functional stakeholders.

• Develop and socialize dashboards, tools, and self-serve data products that help teams answer product questions quickly.

• Help establish data science standards and best practices for measuring AI platform performance and developer success.

• Partner with other data scientists across the company to share learnings and raise the bar on measurement and decision-making.

Requirements

• 10+ years of experience in data science roles within product or technology organizations (platform or developer-facing experience is a plus).

• Expertise in statistics and causal inference, applied in both experimentation and observational studies.

• Expert-level SQL and proficiency in Python for analytics, modeling, and experimentation.

• Proven experience designing and interpreting experiments and making statistically sound recommendations.

• Experience building datasets, metrics, and data pipelines that power production decision-making.

• Experience developing and extracting insights from business intelligence tools (e.g., Tableau) and building self-serve solutions.

• Strong product sense and an impact-driven mindset: you turn ambiguity into crisp frameworks that drive roadmap decisions.

• Ability to build relationships with diverse stakeholders and cultivate strong partnerships across Product, Engineering, Research, and GTM teams.

• Strong communication skills, including the ability to bridge technical and non-technical audiences.

• Ability to operate effectively in a fast-moving, ambiguous environment with limited structure.

Nice to Have

• Are consistently among the first to adopt the latest AI tools, you use them daily to increase your own throughput, and you proactively turn them into

XML job scraping automation by YubHub

Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Data Science

Compensation

• $293K – $325K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Role

We’re hiring a Data Scientist to support our Marketing Innovation pod, a cross-functional team building the internal tools and agentic systems that fundamentally change how we do marketing and serve customers.

We build product-like systems that:

• Deliver high-touch, consultative experiences to millions of SMB customers through agentic lifecycle and sales experiences

• Adapt messaging, creative, and outreach using real-time behavioral signals

• Power intelligent routing, targeting, and engagement decisions at scale, with minimal human-in-the-loop

In this role, you’ll be embedded with Product and Engineering to ensure these systems drive measurable business outcomes.

What You’ll Do

• Define success metrics for agentic marketing systems (e.g., incremental pipeline generated, conversion lift, rep hours saved), including leading indicators that enable weekly iteration.

• Design measurement and experimentation frameworks for always-on systems across lifecycle automation, creative generation, targeting, and routing — using holdouts, staged rollouts, and quasi-experimental methods when needed.

• Partner with PMs and engineers to instrument, evaluate, and monitor launches so every meaningful release has observability and a credible read on incremental value.

• Translate behavioral and model-driven signals into decisions: what to scale, where to intervene, and how to allocate human and compute attention across segments.

• Build repeatable decision loops (pre-launch criteria → post-launch read → next action) that convert analysis into shipped changes.

What We’re Looking For

• 10+ years in a quantitative role (e.g., Data Science, Decision Science), ideally at a product-led company supporting B2B growth, with exposure to SMB or scaled self-serve motions.

• Deep grounding in experimentation, causal inference, and applied statistics, with experience designing and interpreting tests in real-world, always-on environments.

• Strong technical fluency in SQL and Python, including working directly with messy, incomplete behavioral data to quantify impact.

• Proven track record of translating results into shipped decisions (product, lifecycle, targeting, routing).

• Strong business judgment and a bias toward action: able to scope ambiguous problems, define success, and move quickly from insight to strategy.

• Excellent communicator and partner to PMs/Engineers; comfortable influencing stakeholders and presenting recommendations to senior leadership.

Nice to Have

• Familiarity with large language models and AI-assisted operations platforms

• Experience working on operational automation and decision systems (routing, prioritization, optimization)

• Experience operating in early-stage or rapidly evolving environments, including building measurement and experimentation frameworks from scratch.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

XML job scraping automation by YubHub

Location

San Francisco

Employment Type

Full time

Department

Data Science

Compensation

• $255K – $490K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance related bonus for eligible employees and benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick and safe time (1 hour per 30 hours worked)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

About the Team

Integrity Data Science sits at the centre of OpenAI’s mission to deploy powerful AI responsibly. We help ensure people can trust our products by building measurement systems, experimentation practices, and detection/mitigation strategies that protect OpenAI and our users from misuse, fraud, and evolving adversarial behaviours.

As the scope and urgency of Integrity work expands across product surfaces and go-to-market motion, we’re hiring a dedicated Data Science Manager to scale the team, strengthen execution across multiple Integrity domains, and deepen partnership with Product, Engineering, Operations, and adjacent orgs (e.g., Growth, Ads).

This role is based in our San Francisco HQ (in-office).

About the Role

As Data Science Manager, Integrity, you will lead a team of data scientists working across trust & safety, fraud prevention, risk analysis, measurement, and modeling. You’ll be accountable for building a high-performing DS function that can keep pace with fast-moving threats—and for shaping the analytical strategy that informs how OpenAI detects, measures, and mitigates integrity risks at scale.

This is a highly cross-functional leadership role. You’ll help set the roadmap with Integrity Product/Engineering leaders, evolve team structure and operating rhythms, raise the bar on technical rigor (experimentation, causal inference, modeling, metrics), and develop a culture of proactive, high-leverage impact. Many of the challenges in this space are emergent—new misuse patterns appear as the technology and ecosystem evolves—so this role requires strong judgment, comfort with ambiguity, and an ability to build systems that scale.

In this role, you will:

• Lead and scale a high-impact Integrity Data Science team—hiring, coaching, and developing DS ICs (and potentially future managers) while setting a strong technical and cultural bar.

• Drive strategy across multiple Integrity domains (policy enforcement, bot detection, fraud prevention, IP theft, risk measurement, abuse prevention), balancing near-term response with durable systems.

• Build and institutionalize analytical rigor: clear metric frameworks, experimentation standards, monitoring/alerting, and repeatable evaluation approaches for Integrity interventions.

• Partner deeply with Product & Engineering to shape roadmaps, prioritize the right bets, and translate ambiguous risk signals into practical product and platform decisions.

• Evolve team structure and operating model as the org scales—defining ownership boundaries, improving processes, and creating leverage through better tooling and AI-assisted workflows.

• Enable cross-org outcomes, supporting partners outside Integrity (e.g., Growth, Ads, GTM) where integrity risks intersect with product and business goals.

• Communicate clearly with senior leadership, synthesizing complex tradeoffs, surfacing risk, and driving alignment on priorities and success metrics.

• Push the team toward an AI-leveraged operating mode, using modern tooling and model capabilities to accelerate detection, triage, analysis, and iteration.

You might thrive in this role if you:

• Have deep experience leading and scaling Data Science teams, ideally in trust & safety, fraud/abuse, security, risk, or other adversarial problem spaces in fast-moving environments.

• Bring strong technical grounding across modern DS techniques (experimentation, causal inference, anomaly detection, risk modeling, measurement design) and can coach others to execute with rigor.

• Have a track record of building durable partnerships across DS, Engineering, Product, and Operations—able to influence without authority and create shared accountability.

• Are excellent at hiring, mentoring, and developing technical talent, and can build a culture that is both high-bar and supportive.

• Can translate messy, evolving threats into clear frameworks, metrics, and decisions—

XML job scraping automation by YubHub

Location

New York City; San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Data Science

Compensation

• $230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

Benefits

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

OpenAI’s User Operations organization is building the data and intelligence layer behind AI-assisted operations — the systems that decide when automation should help users, when humans should step in, and how both improve over time. Our flagship platform is transforming customer support into a model for “agent-first” operations across OpenAI.

About the Role

As a Data Scientist on User Operations, you’ll design the models, metrics, and experimentation frameworks that power OpenAI’s human-AI collaboration loop. You’ll build systems that measure quality, optimize automation, and turn operational data into insights that improve product and user experience at scale. You’ll partner closely with Support Automation Engineering, Product, and Data Engineering to ensure our data systems are production-grade, trusted, and impactful.

This role is based in San Francisco or New York City. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees.

Why it matters

Every conversation users have with OpenAI products produces signals about how humans and AI interact. User Ops Data Science turns those signals into insights that shape how we support users today and design agentic systems for tomorrow. This is a unique opportunity to help define how AI collaboration at scale is measured and improved inside OpenAI.

In this role, you will:

• Build and own metrics, classifiers, and data pipelines that determine automation eligibility, effectiveness, and guardrails.

• Design and evaluate experiments that quantify the impact of automation and AI systems on user outcomes like resolution quality and satisfaction.

• Develop predictive and statistical models that improve how OpenAI’s support systems automate, measure, and learn from user interactions.

• Partner with engineering and product teams to create feedback loops that continuously improve our AI agents and knowledge systems.

• Translate complex data into clear, actionable insights for leadership and cross-functional stakeholders.

• Develop and socialize dashboards, applications, and other ways of enabling the team and company to answer product data questions in a self-serve way

• Contribute to establishing data science standards and best practices in an AI-native operations environment.

• Partner with other data scientists across the company to share knowledge and continually synthesize learnings across the organization

You might thrive in this role if you have:

• 10+ years of experience in data science roles within product or technology organizations.

• Expertise in statistics and causal inference, applied in both experimentation and observational causal inference studies.

• Expert-level SQL and proficiency in Python for analytics, modeling, and experimentation.

• Proven experience designing and interpreting experiments and making statistically sound recommendations.

• Experience building data systems or pipelines that power production workflows or ML-based decisioning.

• Experience developing and extracting insights from business intelligence tools, such as Mode, Tableau, and Looker.

• Strategic and impact-driven mindset, capable of translating complex business problems into actionable frameworks.

• Ability to build relationships with diverse stakeholders and cultivate strong partnerships.

• Strong communication skills, including the ability to bridge technical and non-technical stakeholders and collaborate across various functions to ensure business impact.

• Ability to operate effectively in a fast-moving, ambiguous environment with limited structure.

• Strong communication skills and the ability to translate complex data into stories for non-technical partners.

Nice-to-haves:

• Familiarity with large language models or AI-assisted operations

XML job scraping automation by YubHub

Location

San Francisco

Employment Type

Full time

Department

Data Science

Compensation

• $293K – $515K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

Benefits

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

OpenAI’s mission is to ensure AI benefits all of humanity. ChatGPT for Work supports that mission by helping more people access real leverage from AI in their day-to-day jobs—so they can spend less time on busywork and coordination, and more time on the work that’s meaningful and additive. We’re building an AI-native workspace where AI acts as a superassistant for everyday tasks and a coworker you can hand work off to—then review, edit, and approve with confidence.

About the Role

As the Data Scientist for ChatGPT for Work, you’ll shape product strategy through data: uncover the user problems most worth solving, form sharp hypotheses about what will move team and business outcomes, and influence what we build next by presenting compelling recommendations grounded in rigorous evidence. You’ll be the DRI for the Work insight → strategy → experiment → decision loop—defining what “success” means for teams, pinpointing the highest-leverage adoption and retention bottlenecks, and turning signals into clear product direction.

You’ll partner closely with Product, Engineering, Research, and Finance to ensure our metrics are trusted, our experimentation is rigorous, and our insights turn into shipped improvements.

Responsibilities

• Own the core KPI framework for ChatGPT for Work, spanning onboarding, activation, engagement, retention, and expansion, as well as quality/trust guardrails.

• Build end-to-end funnels that identify where individuals and teams succeed or get stuck, from first workspace setup through repeat usage and long-term team adoption and value creation.

• Define and operationalize “time-to-value” and collaboration loop metrics, and connect them to business outcomes.

• Design and evaluate experiments and rollouts to quantify the impact of product changes across key Work surfaces and flows.

• Partner with product and engineering teams to improve instrumentation, data quality, and metric definitions so decisions are fast and correct.

• Translate complex analysis into clear, compelling insights that shape product strategy and roadmap decisions.

• Help establish data science standards and best practices for measuring human–AI collaboration and AI-native work outcomes.

• Partner with other data scientists across the company to share learnings and raise the bar on measurement, experimentation, and decision-making.

Requirements

• 10+ years in data science / analytics in in high-velocity product environments

• Direct experience working on B2B products (SaaS, collaboration/workspace, developer tools, or enterprise)

• Expert SQL + strong Python

• Strong experimentation + causal inference judgment (incl. when clean A/B tests aren’t feasible)

• Strong product sense/taste: can turn messy signals into crisp hypotheses and roadmap direction

• Proven ability to inspire and influence PM/Eng/Design + leadership through data storytelling

• Autonomous operator who sets the insights/measurement agenda

• Excellent executive communication; thrives in ambiguous, fast-moving environments

• AI-native operator (non-negotiable): “super AI-pilled”—first to adopt new AI tools, uses them daily to increase throughput, and turns them into durable org workflows

Nice-to-haves

• Experience with agentic and/or AI-native B2B products (agents, copilots, workflow automation, AI collaboration)

• Experience measuring AI product quality, trust, and human-AI interaction signals

• Familiarity with enterprise admin/security constraints and how they shape adoption

• Experience with

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Data Scientist, Codex

Location

San Francisco

Employment Type

Full time

Department

Data Science

Compensation

• $230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance related bonus for eligible employees and benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick and safe time (1 hour per 30 hours worked)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

Codex is OpenAI’s first-party developer product focused on agentic software engineering. We’re building tools that help engineers design, write, test, and ship code faster—safely and at scale. We partner tightly with research and product to translate model advances into tangible developer productivity.

About the Role

As a Data Scientist on Codex, you will measure and accelerate product-market fit for AI developer tools. You’ll define what “developer productivity” means for our product, run experiments on new coding models and UX, and pinpoint where the model helps or hurts across languages and tasks. Your insights will directly shape how an entire industry builds software.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will

• Embed with the Codex product team to discover opportunities that improve developer outcomes and growth

• Design and interpret A/B tests and staged rollouts of new coding models and product features

• Define and operationalize metrics such as suggestion acceptance, edit distance, compile/test pass rates, task completion, latency, and session productivity

• Build dashboards and analyses that help the team self-serve answers to product questions (by language, framework, repo size, task type)

• Diagnose failure modes and partner with Research on targeted improvements (model quality signals, user feedback, evals)

You might thrive in this role if you have

• 5+ years in a quantitative role at a developer-facing or high-growth product

• Fluency in SQL and Python; comfort with experiment design and causal inference

• Experience defining product metrics tied to user value

• Ability to communicate clearly with PM, Eng, and Design—and to influence product direction

You could be an especially great fit if you have

• Strong programming background; ability to prototype, run simulations, and reason about code quality

• Familiarity with IDE/extension telemetry or developer tooling analytics

• Prior experience with NLP/LLMs, code models, or evaluations for generative coding

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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Data Scientist, Business

Location

San Francisco

Employment Type

Full time

Department

Data Science

Compensation

• $255K – $405K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Business Data Science team uses data and analytics to optimize business performance, drive growth, and foster meaningful partnerships, with the goal of ensuring the sustained and impactful expansion of OpenAI's initiatives to maximize the benefits of AGI for all of humanity. We partner with Sales (GTM), Marketing, Partnerships, Support, Finance, Product, and Growth.

About the Role

As a member of our Business Data Science team, you will help build a data-driven culture around insight generation, decision making, and strategy at OpenAI. This role is focused on driving customer success within our business products (ChatGPT Team, ChatGPT Enterprise, and API). You will work on projects such as identifying opportunities for interventions within a customer lifecycle to drive activation & onboarding, identifying target audiences for new feature launches, and measuring the efficacy of emails, events, and other interventions to drive ongoing engagement with our products.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Embed with our Customer Success organization as a trusted partner, uncovering new ways to drive customer adoption and engagement of our business products.

• Establish key metrics, run experiments, and perform analysis to help us understand the incrementality of our efforts to drive adoption/engagement.

• Proactively surface insights and opportunities to drive engagement and growth.

• Build tools and systems for stakeholders to self-serve routine data and insights freeing up time to work on more leveraged analyses.

• Become an expert in OpenAI’s data and systems. Through partnership with Data Eng, Finance and other business teams, you will self-serve all the underlying data for our business and derive insights from them.

• Partner with other data scientists across the company to share knowledge and continually synthesizing learnings across the organization

You might thrive in this role if you have:

• At least 7+ years of experience in Data Science roles within dynamic, outcome-driven organizations.

• Expertise in statistics and causal inference, applied in both experimentation and observational causal inference studies.

• Proficiency in quantitative programming languages, such as Python and R.

• Expertise in SQL, with extensive experience extracting large datasets and designing ETL workflows.

• Experience using business intelligence tools, such as Mode, Tableau, and Looker.

• Strategic and impact-driven mindset, capable of translating complex business problems into actionable frameworks.

• Ability to build relationships with diverse stakeholders and cultivate strong partnerships.

• Strong communication skills, including the ability to bridge technical and non-technical stakeholders and collaborate across various functions to ensure business impact.

• Ability to craft clear data stories using decks, memos, and dashboards to drive decision-making at every level.

• Best-in-class attention to detail and unwavering commitment to accuracy.

• Proven track record in solving problems within Finance, Marketing, Partnerships, Sales, Support, or other GTM areas.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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San Mateo, CA, United States Data Science & Analytics ID: 5750

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.

At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.

A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.

The Data Science & Analytics organization's mission is to increase our speed, frequency and acumen of making decisions at scale by instilling a data-influenced approach to building products. We cover a wide area of the data spectrum including analytical data engineering, product analytics, causal inference, economics, statistical modeling and machine learning. Aligned and partnering with product verticals, we use this extensive toolbelt to discover new opportunities and unmet use cases, influence and shape the product roadmap and prioritization, build data products and measure impact on our community of players and developers.

This is a temporary, part-time position requiring no more than 20 hours per week _for a 3-month duration, with possibility to extend._

Teams Hiring for this role:

• Foundation AI: Our AI evaluation team focuses on generating high-quality models and consistently improving our evaluation models.
• Safety: Managing account relationships and the real-time morphing of linguistic mapping.
• Economy: Drive creator success and growth by exploring marketplace structure and pricing.

You Will:

• Collaborate with data scientists and engineers to research and develop advanced data analytics, causal inference, experiment design and machine learning solutions to power the business and product innovations.
• Conduct in-depth research to address complex data-related challenges.
• Work on projects that have a real impact on our products, services, and business strategy.
• Apply your work to expedite product innovations, including in-experience experiments, friend recommendations, and dynamic resource allocation for experience servers
• Present your findings and recommendations to both technical and non-technical stakeholders.

You Have:

• Possessing or pursuing a PhD degree in a quantitative field such as Statistics, Applied Math, Computer Science, Economics, or Computational Social Science, Operations Research, Computer Engineering, Electrical Engineering.
• At least 1 year of experience doing causal inference or machine learning or experiment design via research or prior internship.
• Proficiency in one or more programming languages (e.g., SQL, Python or R)
• Proficiency in big data query/processing languages and tools such as SQL, Hive, Spark, or Airflow.
• Passion for applying scientific rigor to advance dynamic consumer products.
• Experience in developing production solutions is a plus.
• Experience with ML modeling

You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.

For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual hourly rate could fall outside of this expected range. This pay range is subject to change and may be modified in the future. _Please note that not all benefits shown on this page are applicable to internship opportunities._

Hourly Pay Range

$64—$64 USD

Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).

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San Mateo, CA, United States

Early Career

ID: 5825

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.

At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.

We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.

A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.

WHY DATA SCIENCE & ANALYTICS?

The Data Science & Analytics organization's mission is to increase our speed, frequency, and acumen in making decisions at scale by instilling a data-influenced approach to building products. We cover a wide area of the data spectrum, including analytical data engineering, product analytics, experimentation, causal inference, statistical modeling, and machine learning. Aligned and partnered with product verticals, we use this extensive tool belt to discover new opportunities and unmet use cases, influence and craft the product roadmap, and prioritize, build data products, and measure impact on our community of players and developers.

WHY GENERATIVE AI?

Our team’s mission is to enable Roblox Creators to bring GenAI capabilities to millions of users. We drive this innovation with a core commitment to safety, responsibility, and quality.

As a Data Scientist, you will play a critical role in evaluation and optimization for user-facing GenAI systems (such as text, image, video, 3D, 4D). You will define how we measure safety, responsibility, quality, and efficiency. You will combine annotation analysis, design of experiments, causal inference, model-based evaluation methods (such as LLM-as-a-judge), optimization algorithm, and AI models to drive product decisions and model improvements.

You Will:

• Develop Evaluation Frameworks: Design and operationalize rigorous evaluation systems for either GenAI features (text, image, video, 3D, 4D). This includes eval experiment design, dataset design, label reliability analysis, and implementing and finetuning LLM-as-judge methods.

• Run Rigorous Experiments: Conduct online experiments (A/B tests) and causal inference to quantify the impact of GenAI features. You will identify opportunities, measure lift, and ensure statistical rigor.

• Define Success Metrics: Partner with cross-functional teams to define leading/lagging indicators for GenAI feature user satisfaction, business success, and safety.

• Build Automated Systems: Research and apply state-of-the-art methodologies to build reproducible evaluation tooling that lift rigor and efficiency across the company.

• Conduct Applied Research at the Frontier: Maintain an active pulse on the intersection of Gen AI and Data Science. You will innovate on methodology and techniques to solve unique business challenges while contributing to the broader field in the technical community.

You Have:

• Possess or pursuing a PhD or equivalent in Statistics, Economics, Computer Science, Applied Math, Physics, Engineering, or a related quantitative field.

• Technical Proficiency: Strong proficiency in SQL (Hive/Spark) for manipulating large datasets and scripting languages (Python or R) for analysis and modeling.

• Experimentation and Causal Inference: A solid grounding in experimentation, causal inference, and statistical analysis, including test design and metric design for feature impact.

• Problem Solving: A demonstrated track record of framing ambiguous problems, designing analytical approaches, and solving open-ended data science problems that drive business impact.

• Learning Agility: Ability to effectively and responsibly use AI tools to enhance productivity and a passion for continuously improving methods in a fast-evolving field.

• GenAI Familiarity: Familiarity with GenAI models and safety/quality evaluation methods. Expertise in the model training lifecycle is a plus (e.g., fine-tuning, RLHF, or synthetic data generation).

• Applied Research Background: A track record of applied research or publications in relevant technical fields is highly valued.

You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.

For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page.

Annual Salary Range

$185,860—$221,380 USD

Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).

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Microsoft are looking for a talented Applied Scientist II at their Redmond office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

About the Role

Our team focuses on understanding and predicting how the user interacts with the ads on the search results page. The probability that a user will click on an ad is one of the most critical inputs used in ranking the ads. Similarly, the probability of interacting with the advertiser’s page is important for measuring advertiser and user satisfaction. This position as an Applied Scientist II is for the modeling team, which builds machine learned models for predicting such events.

Accountabilities

• Designing and building efficient models for predicting user interactions with ads and advertiser’s pages.
• Designing and overseeing large-scale, long-term experiments to improve the health of the marketplace using advanced statistics and machine learning.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research).

Technical skills:

• Statistical machine learning, deep learning, data mining, causal inference, information retrieval, game theory, mechanism design, optimization and Bayesian inference.

Personal attributes:

• Excellent problem solving and data analysis skills, effective communication skills, both verbal and written.

Benefits

• Competitive salary.
• Comprehensive benefits package.
• Opportunities for professional growth and development.

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Microsoft are looking for a talented Principal Product Manager - Commerce at their Redmond office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI-native shopping. You'll work directly with leadership to shape the company's direction in the commerce and shopping markets.

About the Role

As a Principal Product Manager - Commerce, you'll be responsible for understanding user and business needs, defining clear requirements, and driving execution across engineering, design, and partner teams. You'll set strategy, align stakeholders, manage delivery through launch and iteration, and ensure outcomes are measurable, high-quality, and scalable. In this role, you will learn how to operate lean teams that obsess over users, make high-quality decisions grounded in evidence, and ship continuously. You'll develop the ability to move quickly from insight to impact, delivering real, immediate value to users. As the AI landscape evolves at an unprecedented pace, you'll gain hands-on experience working at the center of that transformation, building products at the heart of how AI is reshaping the world.

Accountabilities

• Influence others to drive business impact across the organization and track success criteria for multiple feature areas across divisions.
• Identify long-term investment opportunities and engage partners to garner support for those feature areas.
• Translate business goals into strategy, user experience and technical requirements in close collaboration with Applied Science, Data Science, Engineering, UX and Product Marketing/Sales teams.

The Candidate we're looking for

Experience:

• 8+ years experience in product/service/program management or software development.

Technical skills:

• 5+ years experience with data analytics and experimentation tools with working knowledge of SQL and coding skills (Python, Javascript).

Personal attributes:

• Proven ability to collaborate and contribute to a positive, inclusive work environment, fostering knowledge sharing and growth within the team.

Benefits

• Product Management IC5 – The typical base pay range for this role across the U.S. is USD $139,900 – $274,800 per year.
• Certain roles may be eligible for benefits and other compensation.
• Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

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Microsoft are looking for a talented Applied Scientist II at their Mountain View office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising search results page advertising technology. You'll work directly with leadership to shape the company's direction in the online advertising market.

About the Role

Our team focuses on understanding and predicting how the user interacts with the ads on the search results page. The probability that a user will click on an ad is one of the most critical inputs used in ranking the ads. Similarly, the probability of interacting with the advertiser’s page is important for measuring advertiser and user satisfaction. This position as an Applied Scientist II is for the modeling team, which builds machine learned models for predicting such events.

Accountabilities

• Designing and building efficient models for predicting user interactions with ads and advertiser’s pages.
• Designing and overseeing large-scale, long-term experiments to improve the health of the marketplace using advanced statistics and machine learning.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research) OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.

Technical skills:

• Statistical machine learning, deep learning, data mining, causal inference, information retrieval, game theory, mechanism design, optimization and Bayesian inference.

Personal attributes:

• Excellent problem solving and data analysis skills, effective communication skills, both verbal and written.

Benefits

• Competitive salary
• Comprehensive benefits package
• Opportunities for professional growth and development

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Microsoft AI are looking for a talented Senior Data Scientist at their Beijing office. This role sits at the intersection of product, data science, and engineering to define success metrics, design rigorous experiments, build scalable analytics frameworks, and generate actionable insights that improve user experience, product quality, and business outcomes.

About the Role

As a Senior Data Scientist, you will work at the intersection of product, data science, and engineering to define success metrics, design rigorous experiments, build scalable analytics frameworks, and generate actionable insights that improve user experience, product quality, and business outcomes. You will partner closely with Product Managers, Engineers, Designers, and cross-functional stakeholders to drive data-informed decisions from concept to launch. As part of Microsoft AI, you will contribute to building intelligent and trustworthy Copilot experiences that are measurable, explainable, and continuously improving at scale.

Accountabilities

• Define and evolve product health metrics for Copilot experiences (e.g., quality, engagement, retention, latency, reliability, and task success).
• Design and execute A/B tests and quasi-experimental analyses to evaluate feature impact and guide roadmap prioritization.

The Candidate we're looking for

Experience:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Technical skills:

• Solid hands-on skills in Python and SQL; experience with large-scale data processing and analytical workflows.
• Solid foundation in statistical modeling, experimentation design, hypothesis testing, and causal inference.

Personal attributes:

• Solid problem-solving skills and the ability to drive end-to-end analytical projects with high ownership.
• Excellent communication skills in English (written and verbal), with the ability to influence cross-functional partners.

Benefits

• Starting January 26, 2026, Microsoft AI employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week.
• Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
• Commitment to high standards in data quality, privacy, and responsible AI practices.

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Microsoft AI are looking for a talented Senior Data Scientist at their Suzhou office. This role sits at the heart of shaping the next generation of AI-powered experiences across devices and surfaces. You'll work directly with leadership to define success metrics, design rigorous experiments, build scalable analytics frameworks, and generate actionable insights that improve user experience, product quality, and business outcomes.

About the Role

In this role, you will work at the intersection of product, data science, and engineering to define success metrics, design rigorous experiments, build scalable analytics frameworks, and generate actionable insights that improve user experience, product quality, and business outcomes. You will partner closely with Product Managers, Engineers, Designers, and cross-functional stakeholders to drive data-informed decisions from concept to launch. As part of Microsoft AI, you will contribute to building intelligent and trustworthy Copilot experiences that are measurable, explainable, and continuously improving at scale.

Accountabilities

• Define and evolve product health metrics for Copilot experiences (e.g., quality, engagement, retention, latency, reliability, and task success).
• Design and execute A/B tests and quasi-experimental analyses to evaluate feature impact and guide roadmap prioritization.

The Candidate we're looking for

Experience:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Technical skills:

• Solid hands-on skills in Python and SQL; experience with large-scale data processing and analytical workflows.
• Solid foundation in statistical modeling, experimentation design, hypothesis testing, and causal inference.

Personal attributes:

• Solid problem-solving skills and the ability to drive end-to-end analytical projects with high ownership.
• Excellent communication skills in English (written and verbal), with the ability to influence cross-functional partners.

Benefits

• Starting January 26, 2026, Microsoft AI employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week.
• Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
• This role requires effective collaboration across regions and time zones.
• Ability to work in a fast-paced environment with evolving priorities and ambiguous problem spaces.
• Commitment to high standards in data quality, privacy, and responsible AI practices.

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What you'll do

• Partner with design and product management leaders to break down ambiguous problems, identify key business opportunities, and leverage data to establish essential metrics and deliver insights that will drive and shape the strategic direction.
• Transform raw data into data models, production metrics, scaled reporting, and insights to improve user experience and engagement.

What you need

• 7+ years of industry or relevant experience, with a good understanding of live service video games
• Strong product intuition and ability to shape strategy for a complex ecosystem
• Demonstrated background in influencing products by applying data and measurement to drive alignment

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What you'll do

• Partner with design and product management leaders to break down ambiguous problems, identify key business opportunities, and leverage data to establish essential metrics and deliver insights that will drive and shape the strategic direction.
• Transform raw data into data models, production metrics, scaled reporting, and insights to improve user experience and engagement.

What you need

• 7+ years of industry or relevant experience, with a good understanding of live service video games
• Strong product intuition and ability to shape strategy for a complex ecosystem

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