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.

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You will help build a worldwide dataset for our exploration program, with careful attention to identifying and quantifying uncertainty in the data and predictions.

You will create models and develop software to accelerate discovery of critical battery metals.

You will join an outstanding team of data scientists and engineers and work closely with (*applicant's) world-renowned geoscientists to incorporate our best understanding of the chemical and physical processes that create ore deposits.

Working with your geoscience colleagues, you will create 2D and 3D geologic predictions, identify exploration targets, design field programs to collect data, and use that data to reduce uncertainty in our predictions and guide the next phase of field work.

Ultimately, your role is to help KoBold make valuable discoveries by building data tools to solve scientific problems.

As one of the early members of this team, you will help build these tools from the ground up.

Responsibilities:

• Help develop KoBold's proprietary software exploration tools.
• Find and curate geophysical, geochemical, geologic, and geographic data and integrate it into KoBold's proprietary data system.
• Build models to make statistically valid predictions about the locations of compositional anomalies within the Earth's crust.
• Create effective visualizations for evaluating model performance and enabling rapid interaction with the underlying data and key features.
• Develop and apply data processing, statistical, and physics-based techniques to geoscientific data , from computer vision to geophysical inversions , and use the results to guide our targeting efforts and inform our acquisition and exploration decisions.
• Present to and collaborate with our external partners and stakeholders.

Qualifications:

• Technical skills, including extensive experience with Python's data science packages and general software engineering practices.
• Collaborative software development (git), and familiarity with software engineering best practices like unit test / integration test suites, and CICD pipelines.
• Cloud computing resources.
• Building predictive models, applying them to different problems, and evaluating and interpreting the results.
• Data from a variety of physical systems.
• Geospatial analyses and visualizations.

Technical knowledge:

• Broad skills in and knowledge of applied statistics and Bayesian inference.
• Substantial understanding of machine learning algorithms.

Training and work experience:

• An advanced degree in the physical sciences, engineering, computer science, or mathematics.
• A minimum work experience of 4 years post PhD or 8 years post MS, ideally as a data scientist or data engineer.
• Experience leading technical teams to apply novel scientific approaches to core business problems.

Work practices and motivation:

• Ability to take ownership and responsibility of large projects.
• Ability to explain technical problems to and collaborate on solutions with domain experts.
• Communicates well on a collaborative, cross-functional team.
• Excitement about joining a fast-growing early-stage company, comfort with a dynamic work environment, and eagerness to take on a range of responsibilities.
• Ability to independently prioritize multiple tasks effectively.
• Intellectual curiosity and eagerness to learn about all aspects of mineral exploration, particularly in the geology domain.
• Enjoys constantly learning such that you are driving insights through using our tools in exploration and willing to work directly with geologists in the field.
• Keen not just to build cool technology, but to figure out what technical product to build to best achieve the business objectives of the company.
• A valid passport and willingness to travel to observe our work at Mingomba or at an exploration site around the world.

Preferred skills include creating machine learning models on geospatial data, geostatistics, image processing or computer vision, and distributed computing applications for machine learning and other computations.

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You will join an outstanding team of data scientists and engineers and will work closely with KoBold's world-renowned geoscientists to incorporate our best understanding of the chemical and physical processes that create ore deposits. Working with your geoscience colleagues, you will create 2D and 3D geologic predictions, identify exploration targets, design field programs to collect data, and use that data to reduce the uncertainty in our predictions and guide the next phase of field work.

Ultimately, your role is to help KoBold make valuable discoveries by building data tools to solve scientific problems. As one of the early members of this team, you will help build these tools from the ground up.

Responsibilities:

• Help develop KoBold's proprietary software exploration tools.
• Find and curate geophysical, geochemical, geologic, and geographic data and integrate it into KoBold's proprietary data system.
• Build models to make statistically valid predictions about the locations of compositional anomalies within the Earth's crust.
• Create effective visualizations for evaluating model performance and enabling rapid interaction with the underlying data and key features.
• Develop and apply data processing, statistical, and physics-based techniques to geoscientific data , from computer vision to geophysical inversions , and use the results to guide our targeting efforts and inform our acquisition and exploration decisions.
• Present to and collaborate with our external partners and stakeholders.

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Your Contribution:

• Work with subject matter experts to drive AI Technology into business processes
• Help establish and maintain data governance programs across enterprise applications
• Lay the foundation for data-based decision utilizing AI Technologies
• Work with a team of highly talented individuals to understand and support the data needs of our business.

Responsibilities:

• Work with subject matter experts to drive AI Technology into business processes
• Help establish and maintain data governance programs across enterprise applications
• Lay the foundation for data-based decision utilizing AI Technologies
• Work with a team of highly talented individuals to understand and support the data needs of our business.

Requirements:

• Experience building predictive models, especially classification
• Excellent understanding of machine learning techniques and AI
• Expertise in SQL and Python, experience with NoSQL is a plus
• A self-driven ownership mindset with a natural curiosity and excellence in finding solutions to ambiguous problems
• Strong analytic skills related to working with unstructured datasets
• Experience with EDWs or data lakes a plus
• Experience with AWS cloud services
• Junior/Senior pursuing a degree in Data Science/Analytics, Computer Science (focus on AI or Machine Learning), Information Systems/AI or related fields

Benefits:

• Flexible work arrangements
• Opportunities for professional growth and development
• Collaborative and dynamic work environment
• Recognition and rewards for outstanding performance

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Growth - Emails, Notifications and Lifecycle

Location

New York City

Employment Type

Full time

Location Type

Hybrid

Department

-

Compensation

• $239K – $265K • 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 Growth team drives user and revenue growth across ChatGPT’s consumer and business segments worldwide. We operate across the full funnel - from awareness and acquisition through activation, retention, and expansion - using a combination of surface optimizations, in-product funnel improvements, actionable insights, disciplined experimentation, global performance marketing, and AI-powered workflows.

About The Role

We are seeking a full-funnel ‘growth athlete’ with deep expertise in email, notifications, and lifecycle growth initiatives. You will design and scale this motion and associated campaigns across ChatGPT user journeys with rigorous analytical thinking and high-velocity experimentation. This team thrives on rapid testing, precise measurement, and creative problem solving, always keeping user value at the center of our decision-making.

You will own email/notification/lifecycle growth programs end-to-end—from strategy and creative development to deployment, measurement, and iteration. This role is highly cross-functional and will require close collaboration with engineering, product, data science, and design teams to drive engagement, retention, and monetization through these growth levers.

This role is based in San Francisco, CA or New York, NY. 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:

• Design, launch, and optimize global lifecycle tests and programs (email, push notifications, in-app messaging) to drive activation, engagement, and retention across B2C and B2B segments.

• Own end-to-end messaging strategy, including audience segmentation, personalized journeys, channel selection, and timing to maximize conversion and long-term value.

• Develop testing roadmaps for lifecycle campaigns, running A/B and multivariate experiments to improve open rates, click-through rates, and downstream metrics such as paid conversion, DAU/WAU impact, and LTV.

• Partner with product and engineering to enhance messaging infrastructure (event triggers, experimentation tools, data pipelines) and enable sophisticated targeting and automation.

• Build and maintain funnel performance dashboards to surface opportunities and guide prioritization.

• Collaborate with creative and design teams to craft compelling copy and visuals that reinforce ChatGPT’s brand and value proposition while driving measurable outcomes.

• Leverage AI-powered workflows to personalize communications at scale, using predictive models to optimize content, timing, and delivery.

• Partner with data science to develop attribution models and measure the incremental impact of these growth levers. Share these learnings with the broader organization.

You might thrive in this role if you are/have:

• 8+ years of growth experience spanning both consumer (B2C) and business (B2B) products, with a track record of scaling email, push, and in-app messaging platforms and growth initiatives driving activation, engagement, and paid conversion.

• Deep expertise in audience segmentation, targeting, and multi-channel orchestration (email, push, in-app, SMS) across self-serve consumer funnels and multi-stakeholder B2B purchase journeys.

• Hands-on experience with automation, email, and messaging platforms (e.g., SendGrid, Braze, Iterable, Customer.io, Marketo) and comfort working with APIs, event triggers, and CRM data.

• Proven ability to design and interpret experimentation and measurement frameworks—A/B tests, multivariate testing, and incrementality studies—to optimize lifecycle touchpoints across different customer types.

• Strong analytical skills with the ability to model consumer funnels and lead-to-customer pipelines, leveraging data to identify growth opportunities and measure impact.

• Demonstrated success collaborating with cross-functional teams to drive business outcomes and growth.

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

Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Compensation

• $230K – $255K • 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.

About the Team

The User Operations team is central to ensuring that our customers' experience with our products is nothing short of exceptional. We resolve complex issues, provide technical guidance, and support customers in maximizing value and adoption from deploying our products. We work closely with Sales, Technical Success, Product, Engineering and others to deliver the best possible experience to our customers at scale. OpenAI's customers represent a range of diverse backgrounds and maturity, from early-stage startups to established global enterprises. Given OpenAI’s breakneck shipping cadence and growth—and the expectation that it will only accelerate—transforming our rich support data into real‑time insights and scalable, self‑serve analytics is critical to sustaining exceptional customer experiences on the path to AGI.

About the Role

We’re seeking a Support Data Scientist who will dig deep into user‑support data—surfacing trends, volumes, and friction signals—and turn these findings into actionable insights and always‑on reporting. You’ll design, build, and maintain self‑serve dashboards that keep every stakeholder informed in real time, partnering closely with Data Science and Engineering to ensure clean pipelines, robust models, and scalable tooling. Think proactive friction detection and real‑time service‑health views that help us stay ahead of demand—delivering decision‑grade insights, not just prettier slide decks.

This role is based in San Francisco, California. 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:

• Explore large support and product datasets to uncover trends, volume drivers, and user‑experience pain points, distilling findings into clear, actionable narratives.

• Build, enhance, and maintain self‑serve dashboards and reporting tools, enabling non‑technical teams to answer their own data questions.

• Establish a unified metrics taxonomy for service‑health and performance—and build automated data‑sharing pipelines and scorecards with our BPO partners to ensure everyone operates from the same real‑time view of success

• Leverage LLMs to build bespoke classifiers that automatically label and segment inbound volumes—powering smarter routing, richer self‑serve insights, and swifter root‑cause analysis.

• Partner with Data Engineering to ensure reliable pipelines, implement data‑quality checks, and document sources of truth.

• Jump into high‑priority special projects to conduct bespoke deep‑dive analyses and deliver clear, strategic recommendations to leadership.

• Prototype quickly—leveraging ChatGPT, Jupyter notebooks, Retool, and other tools—to prove value before hardening with Engineering.

• Collaborate with Data Science on predictive models and experimentation, translating results into operational recommendations.

You might thrive in this role if you:

• 8 + years in analytics, business intelligence, or data science (experience with customer support or operations teams is a plus).

• Expert‑level SQL skills and proficiency in Python or R for advanced analysis and automation

• Hands‑on experience designing and maintaining BI dashboards (e.g. Looker, Mode, Tableau, Sundial) with a focus on clarity and self‑serve usability.

• Hands‑on experience fine‑tuning or prompt‑engineering LLMs to build text classifiers, sentiment analysis, or tagging systems.

• Demonstrated ability to translate complex datasets into clear business stories and recommendations for both technical and non‑technical audiences.

• Familiarity with support metrics (SLAs, FCR, deflection) and ability to define service health KPIs.

• Strong cross‑functional communication skills—comfortable collaborating daily with engineers, data scientists, and operations leaders.

• An eye for detail, a zero‑defect mindset, and a bias to

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