This group is also accountable for analytics production, enhancing the infrastructure platform, and delivering analytics content to portfolio and risk management professionals (both within BlackRock and across the Aladdin client community).

The models developed and supported by AFE span a wide array of financial products covering equities, fixed income, commodities, derivatives, and private markets. AFE provides investment insights that range from an analysis of cash flows on a single bond, to the overall financial risk associated with an entire portfolio, balance sheet, or enterprise.

We are looking to hire a senior quant modeler (VP) to join our Private Asset Market Risk Modeling team to drive the development of risk factor models for private market investments.

The Private Asset Market Risk team builds a range of models, including private equity, real estate, credit, infrastructure, and hedge funds, with sophisticated econometric/statistical methods and tools.

These models have real practical value and have a very large footprint of usage across the entire Aladdin client base including portfolio managers, risk managers, and allocators and influence investment activity.

As a result, we place special emphasis on developing models that scale with our growing Analytics' business and ensuring adherence to BlackRock's rigorous standards of model governance.

This individual would have a strong background in quantitative research, have demonstrable project management skills as well as proven experience to work in a team environment as well as collaborating with senior modelers from other groups/regions.

This person is expected to join as an individual contributing to the design and development of our private market risk models, particularly in the private credit (i.e., real estate debt, infrastructure debt) space.

Develop private credit risk factor models and back test, document, and guide new models and methodologies through validation.

Collaborate with partner teams on model productionization

Build and maintain model governance controls, including (but not limited to) model performance monitoring, model documentation, model remediations and supporting internal & external client model validations

Communicate (verbally and in writing) with internal stakeholders and external clients on the design, backtesting, and usage of the models. Discuss model performance regularly, investigate exceptional model performance, diagnose issues and conduct corrective remediations

Extensive experience in quantitative field / statistical modeling. Experience with one or more of the following is preferred: risk factor models and analytics, domain knowledge of fixed income securities, applications of ML/AI techniques.

Master's or PhD degree in a quantitative discipline or one that relates to application of quantitative techniques in finance (financial engineering, math finance, etc.). Master's degree with 5+ years or PhD degree with 3+ years of experience.

A strong background in data-driven quantitative research, econometrics, and empirical asset pricing

Hands-on experience with statistical modeling through software (e.g., Python, R) and strong background in programming. Proficiency with Python is required

Experience with data handling (ETL, data joining with SQL, cleaning, processing, summarizing, descriptive analysis), and building and back-testing statistical and econometric models

Knowledge of investments, portfolio management is preferred

Experience with any version control system (e.g., git) is strongly preferred

Prior work experience in financial modeling (e.g., risk models, analytics, pricing/valuation models) or data science and model deployment to production environment is a plus

Resourcefulness and strong problem-solving skills even in the face of constraints, limited options, or uncertainties

Ability to work effectively with a team of highly motivated individuals

Time and project management skills

Proven track record of guiding junior talent

Positive attitude and ability to work both independently and as a part of a global team in a fast-paced environment

Excellent communication and presentation skills

To help you stay energized, engaged and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.

Our hybrid work model

BlackRock's hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.

About BlackRock

At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children's educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.

This mission would not be possible without our smartest investment – the one we make in our employees. It's why we're dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.

XML job scraping automation by YubHub

You'll have the opportunity to design dashboards with Power BI and Excel, help build end-to-end data pipelines using SQL and Python, and develop your knowledge in statistical modeling, machine learning, and data storytelling. The problems we tackle are challenging and require creativity, critical thinking, and curiosity.

The team works closely with stakeholders across functions to explore questions, tell stories through data, and drive innovation through analytics. You'll be working on projects that have a significant impact on the business and will be able to see the results of your work.

XML job scraping automation by YubHub

Introduction to GPS

The Global Product Solutions (GPS) business at BlackRock aims to deliver innovative investment solutions and services that meet the evolving needs of our clients. Our mission is to provide top-tier financial products and advice to help clients achieve their financial goals.

Your Team and Role

We are looking for a Data Scientist to join our newly established Chief Data Office. You will be responsible for working with our Global Business Intelligence (GBI) team to leverage data and analytics to provide actionable insights, drive strategic decisions, and enhance the overall performance of our products and services. You will collaborate with multidisciplinary teams to develop data-driven solutions that support our global operations.

In addition to your technical skills, you will need to possess excellent people skills. You will interact with various stakeholders, including product managers, analysts, and senior leadership, requiring you to pitch your analysis and explain complex concepts to non-technical audiences. Your ability to communicate effectively and collaborate across different teams will be crucial in driving successful data-driven initiatives and ensuring the alignment of data strategies with business goals.

Your Responsibilities

• Analyze complex datasets to extract meaningful insights and identify trends that impact business performance.
• Develop and implement machine learning models to optimize investment strategies and improve client outcomes.
• Create and maintain data pipelines to ensure the efficient processing and integration of large volumes of data.
• Collaborate with product managers, analysts, and other stakeholders to understand business requirements and translate them into data-driven solutions.
• Present findings and recommendations to senior leadership and other key stakeholders in a clear and concise manner.
• Stay updated on the latest advancements in data science and analytics to continuously enhance our methodologies and tools.

Qualifications and Experience

• Bachelor's or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Proven experience in data analysis, machine learning, and statistical modeling.
• Proficiency in programming languages such as Python, or R and SQL.
• A flexible analytic approach that allows for results at varying levels of precision
• Ability and interest in diving deep into complex data models and ETLs in order to come up with solutions to streamline, scale and automate existing or new processes
• Experience with data visualization tools like Tableau, Power BI, or similar.
• Strong critical thinking skills and the ability to work with large, complex datasets.
• Excellent verbal and written communication, teamwork, and relationship-building skills

Preferred Skills

• Experience in the financial services industry or a related field.
• Understanding of investment products and financial markets.

Our benefits

To help you stay energized, engaged and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.

Our hybrid work model

BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.

About BlackRock

At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children’s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.

XML job scraping automation by YubHub

Responsibilities:

• Run convening operations end-to-end. For each convening, develop invite lists and track outreach; refine presentation materials; build welcome packets and research briefs; coordinate room/travel/dinner logistics; run point for day-of facilitation support; lead post-convening communications.

• Own systematic follow-up. Build systems to track customized post-convening support to participants as they shape and use AI in their own communities.

• Use Claude for innovation and scale. Develop Claude-based systems and tools to develop new ways of convening (Claude interviews participants before a convening to better understand their hopes) and create operational efficiency.

You may be a good fit if you:

• Find the work genuinely interesting, with a deep desire to engage diverse communities on the future of AI.

• Have a strong track record of building scalable operational systems for new programs.

• Have run high-touch external programs, executive convenings, or stakeholder engagement.

• Are comfortable with complex logistics that work across external organizations, internal teams, and executives.

XML job scraping automation by YubHub

At ZoomInfo we encourage creativity, value innovation, demand teamwork, expect accountability and cherish results. If you are a take-charge, take-initiative, get-stuff-done individual we want to talk to you! We have high aspirations for the company and are looking for the right people to help fulfill the dream. We strive to continually improve every aspect of the company and use cutting-edge technologies and processes to delight our customers and rapidly increase revenues.

As the Head of People Analytics & Innovation, you will build and lead ZoomInfo's people analytics function from the ground up. Reporting to the SVP of Organizational Effectiveness, you will own the end-to-end analytics strategy for HR , designing and shipping AI-powered tools, predictive and prescriptive models, dashboards, and research that drive executive decision-making on talent, workforce planning, and employee experience. This is a hands-on, player-coach role: you will set the vision for what we build, write the code to build it, and hire and develop a small, high-impact team (2 ICs) to scale the function. The ideal candidate combines deep technical fluency (Python, statistics, SQL, AI/ML) with the strategic judgment to translate people data into business outcomes and the builder mentality to stand up new capabilities from scratch.

Responsibilities

• Build and lead ZoomInfo's People Analytics & Innovation function, including hiring, developing, and managing two individual contributors
• Define and execute the people analytics strategy and roadmap, translating HR and business priorities into a pipeline of high-impact analytics projects and AI-powered tools
• Design and build custom AI tools, automation, and applications (using Python, MCPs, LLMs, and related technologies) that fundamentally improve how HR operates and makes decisions
• Develop predictive and prescriptive models across workforce planning, attrition/retention, engagement, talent mobility, and employee experience
• Create and maintain executive-ready dashboards, reports, and data visualizations that surface actionable insights for senior leadership
• Own the design, deployment, and analysis of enterprise-wide qualitative instruments (e.g., employee surveys, pulse checks) and synthesize findings into strategic recommendations
• Partner with HR leadership and cross-functional stakeholders to identify high-value analytical questions, scope projects, and deliver data-driven recommendations
• Transform complex analytical findings into clear, compelling narratives and presentations for executive audiences, including the ELT
• Establish scalable data infrastructure, governance frameworks, and measurement processes that enable a self-service analytics culture over time
• Stay current on emerging trends in people analytics, AI/ML, and HR technology , and proactively identify opportunities to apply them at ZoomInfo
• Collaborate with global Compliance, Privacy, Legal, and Risk teams to ensure people analytics initiatives adhere to regulatory requirements, data privacy standards, and internal governance policies
• Partner with enterprise Data Engineering and Infrastructure teams to architect, test, and deploy analytics solutions and AI tools into production environments
• Foster a team culture of experimentation, intellectual curiosity, and bias toward action

Requirements

• 8+ years of progressive experience in people analytics, workforce analytics, HR data science, or a related quantitative field
• 3+ years of experience managing or leading an analytics team, including hiring, coaching, and developing talent
• Demonstrated experience building AI/ML tools, applications, or automation from scratch in a professional setting (not just model training , full product delivery)
• Strong hands-on proficiency in Python and SQL; experience with data manipulation, statistical modeling, and building production-grade scripts and applications
• Experience working with modern AI/ML tools and frameworks (e.g., LLMs, GenAI APIs, MCP architecture, NLP)
• Proven ability to design and execute enterprise survey programs and translate qualitative data into quantitative insights
• Exceptional data storytelling skills , ability to synthesize complex analysis into clear, executive-ready communications
• Experience working with HRIS platforms (e.g., Workday) and analytics/BI tools (e.g., Snowflake, Tableau, Looker, or similar)
• Demonstrated track record of operating as a player-coach: setting strategy while personally delivering technical work
• Bachelor's degree in a quantitative field (e.g., Data Science, Statistics, I/O Psychology, Economics, Computer Science, or related)

Preferred

• Master's or Doctoral degree in a quantitative, business, or social science field (e.g., I/O Psychology, Data Science, Statistics, Organizational Behavior, Economics, Computer Science)
• Experience building a people analytics function or team from scratch
• Familiarity with MCP (Model Context Protocol) architecture and building AI agent workflows
• Experience with Workday RAAS reporting and Snowflake data environments
• Background in building self-service analytics platforms or democratizing data access within an organization
• Experience at a high-growth technology company or a company that has undergone significant organizational transformation

Compensation

The US base salary range for this position is $134,400 - $249,600 + bonus + equity + benefits.

Actual compensation offered will be based on factors such as the candidate's work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process.

XML job scraping automation by YubHub

As a Principal Data Science and Analytics in the team, your major responsibilities include:

Leadership: Mentor data scientists and align work with MAI ecosystem goals, driving technical excellence, innovation, and cross-team collaboration. Data Strategy & Execution: Develop ecosystem data strategies for marketplace and system performance, including standardized data collection, analysis, reporting, and interpretation; validate analytical approaches and results. Advanced Analytics & Measurement: Apply machine learning, statistical modeling, data mining, and experimentation to large datasets; define and deliver metrics that accurately measure user and business value across products and marketplace components. Experimental Design & Implementation: Design and execute experiments across user and demand dimensions; translate strategy into clear, actionable, and measurable plans, sharing progress and results with stakeholders. Collaboration: Partner closely with product, program management, engineering, and business teams to integrate data science solutions into shared platforms and marketplace operations. Performance Optimization: Identify cross-team opportunities for product and process improvement; implement data-driven solutions to improve efficiency, reliability, and user experience. Influence & Decision-Making: Engage stakeholders with clear, compelling, and actionable insights; make independent decisions for the team and handle complex tradeoffs to drive product and service improvements. Technical & Operational Leadership: Develop and standardize processes for data acquisition, quality, and operationalizing ML models; provide expert review of analysis and modeling; lead adoption of new tools and technologies to improve availability, reliability, efficiency, and performance. Standards & Trusted Advisory: Establish and uphold standards, policies, and best practices for high-quality, efficient, and extensible code; influence business, customer, and solution strategy with a solid customer focus; act as a trusted advisor across the ecosystem.

Required Qualifications: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years 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 10+ 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 12+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Preferred Qualifications: 6+ years of experience in at least one of programming languages like Python/R/MATLAB/C#/Java/C++ Great organizational, analytical, data science skills and intuition Fantastic problem solver: ability to solve problems that the world has not solved before Interpersonal skills: cross-group and cross-culture collaboration. Experience with real-world system building and data collection, including design, coding, and evaluation Excellent communication to be able to communicate insights to senior leaders. Experience with driving large collaboration across multiple teams. Experience with communicating with different audiences to provide insights Demonstrated experience in applying statistics, experimentation, and metrics to generate clear actionable insights.

XML job scraping automation by YubHub

As a Principal, Data Science and Analytics in the team, your major responsibilities include:

Leadership: Mentor data scientists and align work with MAI ecosystem goals, driving technical excellence, innovation, and cross-team collaboration. Data Strategy & Execution: Develop ecosystem data strategies for marketplace and system performance, including standardized data collection, analysis, reporting, and interpretation; validate analytical approaches and results. Advanced Analytics & Measurement: Apply machine learning, statistical modeling, data mining, and experimentation to large datasets; define and deliver metrics that accurately measure user and business value across products and marketplace components. Experimental Design & Implementation: Design and execute experiments across user and demand dimensions; translate strategy into clear, actionable, and measurable plans, sharing progress and results with stakeholders. Collaboration: Partner closely with product, program management, engineering, and business teams to integrate data science solutions into shared platforms and marketplace operations. Performance Optimization: Identify cross-team opportunities for product and process improvement; implement data-driven solutions to improve efficiency, reliability, and user experience. Influence & Decision-Making: Engage stakeholders with clear, compelling, and actionable insights; make independent decisions for the team and handle complex tradeoffs to drive product and service improvements. Technical & Operational Leadership: Develop and standardize processes for data acquisition, quality, and operationalizing ML models; provide expert review of analysis and modeling; lead adoption of new tools and technologies to improve availability, reliability, efficiency, and performance. Standards & Trusted Advisory: Establish and uphold standards, policies, and best practices for high-quality, efficient, and extensible code; influence business, customer, and solution strategy with a strong customer focus; act as a trusted advisor across the ecosystem.

Qualifications: Doctorate 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 Master’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ 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 10+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Preferred Qualifications: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years 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 10+ 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 12+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

XML job scraping automation by YubHub

Responsibilities: Mentor data scientists to drive Microsoft Content product analysis and provide insights. Partner closely with Microsoft Content Dev/PM Team and data scientists from other Product (Edge, Copilot, Ads and Search.) Develop Agent for data analysis across Microsoft Content Org. Provide daily analysis, including standardized data collection, analysis, reporting, and interpretation; validate analytical approaches and results. Apply LLM based AI skills, statistical modeling, data mining, and experimentation to large datasets; define and deliver metrics that accurately measure user and business. Design and execute experiments across user and demand dimensions; translate strategy into clear, actionable, and measurable plans, sharing progress and results with stakeholders. Engage stakeholders with clear, compelling, and actionable insights; make independent decisions for the team and handle complex tradeoffs to drive product and service improvements. Develop and standardize AI Agents for data analysis across the whole Microsoft Content and expand to other Product Org. Establish and uphold standards, policies, and best practices for high-quality, efficient, and extensible code; influence business, customer, and solution strategy with a solid customer focus; act as a trusted advisor.

Qualifications: Doctorate 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 Master’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ 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 10+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Preferred Qualifications: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years 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 10+ 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 12+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

#MicrosoftAI

XML job scraping automation by YubHub

As a Principal, Data Science and Analytics in the team, your major responsibilities include:

Leadership: Mentor data scientists and align work with MAI ecosystem goals, driving technical excellence, innovation, and cross-team collaboration. Data Strategy & Execution: Develop ecosystem data strategies for marketplace and system performance, including standardized data collection, analysis, reporting, and interpretation; validate analytical approaches and results. Advanced Analytics & Measurement: Apply machine learning, statistical modeling, data mining, and experimentation to large datasets; define and deliver metrics that accurately measure user and business value across products and marketplace components. Experimental Design & Implementation: Design and execute experiments across user and demand dimensions; translate strategy into clear, actionable, and measurable plans, sharing progress and results with stakeholders. Collaboration: Partner closely with product, program management, engineering, and business teams to integrate data science solutions into shared platforms and marketplace operations. Performance Optimization: Identify cross-team opportunities for product and process improvement; implement data-driven solutions to improve efficiency, reliability, and user experience. Influence & Decision-Making: Engage stakeholders with clear, compelling, and actionable insights; make independent decisions for the team and handle complex tradeoffs to drive product and service improvements. Technical & Operational Leadership: Develop and standardize processes for data acquisition, quality, and operationalizing ML models; provide expert review of analysis and modeling; lead adoption of new tools and technologies to improve availability, reliability, efficiency, and performance. Standards & Trusted Advisory: Establish and uphold standards, policies, and best practices for high-quality, efficient, and extensible code; influence business, customer, and solution strategy with a strong customer focus; act as a trusted advisor across the ecosystem.

Qualifications: Doctorate 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 Master’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ 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 10+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Preferred Qualifications: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years 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 10+ 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 12+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

XML job scraping automation by YubHub

As a Principal Data Science and Analytics in the team, your major responsibilities include:

Leadership: Mentor data scientists and align work with MAI ecosystem goals, driving technical excellence, innovation, and cross-team collaboration. Data Strategy & Execution: Develop ecosystem data strategies for marketplace and system performance, including standardized data collection, analysis, reporting, and interpretation; validate analytical approaches and results. Advanced Analytics & Measurement: Apply machine learning, statistical modeling, data mining, and experimentation to large datasets; define and deliver metrics that accurately measure user and business value across products and marketplace components. Experimental Design & Implementation: Design and execute experiments across user and demand dimensions; translate strategy into clear, actionable, and measurable plans, sharing progress and results with stakeholders. Collaboration: Partner closely with product, program management, engineering, and business teams to integrate data science solutions into shared platforms and marketplace operations. Performance Optimization: Identify cross-team opportunities for product and process improvement; implement data-driven solutions to improve efficiency, reliability, and user experience. Influence & Decision-Making: Engage stakeholders with clear, compelling, and actionable insights; make independent decisions for the team and handle complex tradeoffs to drive product and service improvements. Technical & Operational Leadership: Develop and standardize processes for data acquisition, quality, and operationalizing ML models; provide expert review of analysis and modeling; lead adoption of new tools and technologies to improve availability, reliability, efficiency, and performance. Standards & Trusted Advisory: Establish and uphold standards, policies, and best practices for high-quality, efficient, and extensible code; influence business, customer, and solution strategy with a solid customer focus; act as a trusted advisor across the ecosystem.

Qualifications: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years 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 10+ 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 12+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Preferred Qualifications: 6+ years of experience in at least one of programming languages like Python/R/MATLAB/C#/Java/C++ Great organizational, analytical, data science skills and intuition Fantastic problem solver: ability to solve problems that the world has not solved before Interpersonal skills: cross-group and cross-culture collaboration. Experience with real-world system building and data collection, including design, coding, and evaluation Excellent communication to be able to communicate insights to senior leaders. Experience with driving large collaboration across multiple teams. Experience with communicating with different audiences to provide insights Demonstrated experience in applying statistics, experimentation, and metrics to generate clear actionable insights.

XML job scraping automation by YubHub

Our corporate values, Care, Integrity, Passion, Unity, and Excellence,are at the heart of everything we do. These values define how we operate, how we treat one another, and how we engage with our partners, customers, and the communities we serve.

The Senior Manager, Digital Media Analytics at Valvoline Global turns complex marketing and business data into clear insights that shape communications strategies, deepen audience understanding, and measure impact across earned, owned, and paid channels. This role blends analytical expertise with creative storytelling, partnering closely with commercial, marketing, operations, and finance teams to design and deliver high-impact analytical solutions that uncover opportunities and drive smarter, more effective decisions.

Key Responsibilities Include:

• Lead strategic analytics initiatives to support business planning, performance measurement, and forecasting across global functions.

• Design and deliver dashboards, reports, and data models that communicate key metrics, trends, and actionable insights to cross-functional stakeholders.

• Oversee global Google Analytics (GA4) strategy, implementation, and governance, ensuring accurate tracking, consistent measurement frameworks, and alignment with business objectives.

• Apply statistical analysis, predictive modeling, and data visualization techniques to solve business challenges and uncover growth opportunities.

• Lead cross-functional analytics initiatives by developing project roadmaps, execution plans, and post-project reports that demonstrate impact, ROI, and key learnings.

• Provide briefings, analytics strategies, and measurement frameworks that guide Valvoline Global in data-driven decision-making and align analytics outputs with organizational objectives.

• Develop training materials and lead workshops to strengthen data literacy across teams.

• Collaborate with business leaders to translate questions into analytical problems and deliver actionable, data-driven recommendations.

• Partner with stakeholders to support data quality and infrastructure by conducting audits, identifying improvements, and maintaining clear documentation of data sources, metric definitions, and governance processes.

• Stay current on emerging tools, technologies, and analytics best practices, conducting proof-of-concepts, and recommending new technologies (including BI and AI/ML solutions) based on business value.

What You’ll Need:

• Bachelor’s degree in Analytics, Statistics, Mathematics, Business, Economics, Computer Science, or a related field.

• 8+ years of progressive experience in digital analytics, business intelligence, or data science roles, preferably in a global or matrixed organization.

• Expertise in data visualization tools (e.g., Power BI, Tableau, or equivalent) and data manipulation using SQL, Python, or R.

• Strong experience with predictive analytics, statistical modeling, and machine learning methodologies.

• Proven ability to analyze complex datasets and translate findings into strategic recommendations.

• Exceptional communication and storytelling skills with the ability to translate complex data into clear business insights.

• Strong business acumen with experience working across functions such as Sales, Marketing, Supply Chain, and Finance.

• Demonstrated ability to influence decisions through data in a fast-paced, collaborative environment.

• Ability to work with global teams, with cultural differences, and can work across multiple time zones.

• Passion for social media and awareness of platform best practices.

• Quick learner with a proactive, problem-solving mindset.

• Comfortable in a fast-paced, deadline-driven environment.

What Will Set You Apart:

• Master’s degree preferred.

• Knowledge of data governance and data quality principles.

• Global experience as it relates to data governance and laws.

Benefits That Drive Themselves:

• Health insurance plans (medical, dental, vision).

• Health Savings Account (with employer-base deposit and match).

• Flexible spending accounts.

• Competitive 401(k) with generous employer base deposit and match.

• Incentive opportunity.

• Life insurance.

• Short- and long-term disability insurance.

• Paid vacation and holidays.

• Employee Assistance Program.

• Employee discounts.

• PTO Buy/Sell Options.

• Tuition reimbursement.

• Adoption assistance.

Terms and conditions apply, and benefits may differ depending on position or tenure.

XML job scraping automation by YubHub

Your contribution will have an impact that extends beyond individual projects, shaping the way engineering works, develops and remains efficient at AVL Powertrain Engineering in the long term.

Responsibilities: Analysis of large amounts of data and derivation of relevant insights Development and implementation of ML and statistical algorithms Application of modern data science methods for process and efficiency optimization Building models for structured and unstructured data Creating visualizations and dashboards for data-driven decisions Collaborating with engineering and specialist departments to identify potential Ensuring data quality and best practice standards Providing technical support in pre-sales phases and customer projects

Profile: Completed education (University, University of Technology, University of Applied Sciences) with a focus on mathematics, statistics, computer science or related fields At least 3–5 years of professional experience in data science, industrial ML use cases or related areas Confident use of Python/R/Scala and ML frameworks (e.g. TensorFlow, Spark) Knowledge of statistical modeling and data analysis Experience in building machine learning models Analytical and conceptual thinking, as well as the ability to communicate technical insights to different stakeholders in an understandable way Strong problem-solving skills and high quality awareness Strong communication and teamwork skills, as well as strong organizational skills Very good English skills, German skills are an advantage or at least a willingness to learn German Willingness to travel up to 10 percent of the time (mainly Croatia)

We Offer: Various working time models, flextime and the possibility of working from home Employee discounts for various offers and services Company sports club with more than 30 sections and regular sports events/trips 80% subsidy for public transportation costs or access to a personal parking space Attractive bike leasing program

Annual Remuneration: Due to the Austrian Equal Treatment Act we are obligated to state the annual gross remuneration (full-time) for this position as a basis for negotiation: €61,000.00.

XML job scraping automation by YubHub

Your role will involve analyzing large datasets, developing and implementing machine learning algorithms, and creating visualizations and dashboards for data-driven decision-making. You will also collaborate with engineering and other departments to identify opportunities for improvement and ensure data quality and best practices.

We offer a range of benefits, including flexible working hours, a 80% public transportation reimbursement, and a comprehensive health insurance plan. Our company culture values diversity, equality, and inclusion, and we strive to create a workplace where everyone feels welcome and valued.

If you are interested in this opportunity, please submit your application through our online portal. We look forward to hearing from you!

XML job scraping automation by YubHub

Responsibilities:

• Lead a wide range of projects involving the design and implementation of strategies to reduce trading costs for delta-one and factor products.
• Monitor and manage risks within company guidelines and risk parameters, including operational, portfolio, financing, and basis risk.
• Partner with team members to set the overall direction, design, and architecture of the platform; collaborate with key stakeholders across the business.

Qualifications:

• 6+ years in a trading or execution role.
• Bachelor's degree in Mathematics, Physics, Finance, Economics, Econometrics, Financial Engineering, Operations Research, or similar.
• Experience with factor modeling, transaction cost analysis (TCA) models, statistical modeling, and portfolio analytics.
• Strong operational and event risk management skills; experience managing systematic strategies; familiarity with equity markets/asset classes.
• Familiarity with ETFs, futures, swaps, and vanilla derivatives is a plus (and can be learned on the desk).
• Self-sufficient programming ability in Python and/or kdb+ for analysis and research, plus Git, Unix/Linux, Bash, etc.
• Strong communication skills and the ability to work effectively in a team environment.

The estimated base salary range for this position is $160,000 to $250,000, which is specific to New York and may change in the future. Millennium pays a total compensation package which includes a base salary, discretionary performance bonus, and a comprehensive benefits package.

XML job scraping automation by YubHub

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

XML job scraping automation by YubHub

You’ll define the long-term data science roadmap while partnering closely with Okta’s diverse Product Management teams and executive leadership.

Responsibilities

• Collaborate closely with Product Managers and leadership to provide insights that will help drive product strategy and roadmaps, ensuring decisions are grounded in a data-driven approach.
• Build and deploy statistical and machine learning models (e.g., predicting churn, lifetime value, feature adoption) to forecast user behavior and product growth opportunities in order to help influence roadmap decisions.
• Conduct thorough exploratory analysis on extensive, complex datasets to uncover key drivers of user adoption and engagement, identifying unseen opportunities for significant product improvement.
• Work with data engineering, analytics engineering, and data analysts to shape and enhance the foundational data infrastructure necessary for scalable ML and advanced analytics initiatives.
• Develop and construct usable data sets by integrating and manipulating information from various disparate data sources as needed.
• Translate intricate data insights into clear, compelling narratives for executives, product managers, and engineers, effectively influencing crucial business and product decisions.

Requirements

• 7+ years of experience in data science or ML, including 3–5 years in a senior, staff, or technical leadership role.
• Deep understanding of product analytics and data science frameworks, with a proven history of designing and analyzing product data to solve complex, ambiguous business problems and deliver measurable results.
• Demonstrated ability to apply cutting-edge AI tools to accelerate the discovery of deep, actionable insights from complex product data.
• Demonstrated ability to translate data insights into product impact and formulate strategic, data-driven recommendations.
• Deep expertise in machine learning algorithms (supervised, unsupervised, NLP, forecasting, optimization) and statistical modeling.
• Strong proficiency in Python, SQL, and leading ML libraries.
• Excellent communication skills and a proven ability to influence Product and executive partners.

Nice to Have

• Experience in high-growth SaaS, cybersecurity, identity, or enterprise software environments.
• Prior ownership of ML platform/tooling decisions and evaluations.
• Experience enabling self-service analytics or citizen data science capabilities.

XML job scraping automation by YubHub

As a Power & Resource Efficiency Engineer, you'll sit at the intersection of IT and facilities , building the systems, models, and control loops that optimize how we allocate and consume power, cooling, and physical capacity across our TPU/GPU fleet.

You'll own the technical strategy for turning raw data center capacity into reliable, efficient compute, working across power topology, workload scheduling, and real-time telemetry to push utilization as close to the physical envelope as possible while maintaining our availability commitments.

Responsibilities

• Build models that forecast consumption across electrical and mechanical subsystems, informing capacity planning, energy procurement, oversubscription targets and risks, including statistical modeling of cluster utilization, workload profiles, and failure modes.

• Design IT/OT interfaces that bridge compute orchestration with facility controls, enabling real-time telemetry across accelerator hardware, power distribution, cooling, and schedulers.

• Build and operate load management systems that use power and cooling topology to enable load management and power/thermal-aware placement to maximize throughput while meeting SLOs.

• Partner with data center providers to drive design optimizations and hold them accountable to SLA-grade performance standards, providing technical diligence on partner architectures.

What We're Looking For

• Deep knowledge of data center power distribution and cooling architectures, and how they interact with IT load profiles. Experience with reliability engineering, SLA development, and failure-mode analysis.

• Proficiency in statistical modeling and simulation for infrastructure capacity or power utilization.

• Familiarity with SCADA/BMS/EPMS, telemetry pipelines, and control systems. Experience building software that bridges IT and OT.

• Exposure to accelerator deployments and their power management interfaces strongly preferred.

• Demand response, grid interaction, or behind-the-meter generation experience is a plus.

• Ability to translate between infrastructure engineering, software teams, and external partners.

Required Qualifications

• Bachelor's degree in Electrical Engineering, Mechanical Engineering, Power Systems, Controls Engineering, or a related field.

• 5+ years of experience in data center infrastructure or facility engineering.

• Demonstrated experience with data center power distribution and cooling system architectures.

• Experience building or operating software-based power management, load scheduling, or control systems.

• Proficiency in Python or similar languages for statistical modeling, simulation, or automation of data center infrastructure optimizations.

• Familiarity with SCADA, BMS, EPMS, or industrial control systems and associated protocols (Modbus, BACnet, SNMP).

• Track record of cross-functional collaboration across hardware, software, and facilities teams.

Preferred Qualifications

• Master's or PhD in Controls, Power Systems, or related discipline and 3+ years of experience in data center infrastructure or facility engineering.

• Experience with accelerator-class deployments and their power management interfaces.

• Background in control theory, dynamical systems, or cyber-physical systems design.

• Experience with energy storage, microgrid integration, demand response, or behind-the-meter generation.

• Familiarity with reliability engineering methods.

• Experience with SLA development, availability modeling, or service credit frameworks.

• Exposure to ML/optimization techniques applied to infrastructure or energy systems.

Salary

The annual compensation range for this role is $320,000-$405,000 USD.

Benefits

We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with our team.

XML job scraping automation by YubHub

Key responsibilities include:

• Developing best practices for instrumentation and experimentation to help fulfill our mission to bring everyone the inspiration to create a life they love
• Bringing scientific rigor and statistical methods to the challenges of product creation, development, and improvement with an appreciation for the behaviors of our users
• Building and prototyping analysis pipelines iteratively to provide insights at scale while developing comprehensive knowledge of data structures and metrics, advocating for changes where needed for product development
• Working cross-functionally to build and communicate key insights, and collaborating closely with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest

Requirements include:

• 5+ years of experience analyzing data in a fast-paced, data-driven environment with proven ability to apply scientific methods to solve real-world problems on web-scale data
• Extensive experience solving analytical problems using quantitative approaches, including machine learning, statistical modeling, forecasting, econometrics, or other related fields
• Experience using machine learning and deep learning frameworks, such as PyTorch, TensorFlow, or scikit-learn
• A scientifically rigorous approach to analysis and data, and a well-tuned sense of skepticism, attention to detail, and commitment to high-quality, results-oriented output
• Ability to manipulate large data sets with high dimensionality and complexity; fluency in SQL (or other database languages) and a scripting language (Python or R)
• Excellent communication skills and ability to explain learnings to both technical and non-technical partners
• A team player who's able to partner with cross-functional leadership to quickly turn insights into actions

Relocation assistance is not eligible for this position. The base salary range for this position is $139,764-$287,749 USD, and the position is eligible for equity.

XML job scraping automation by YubHub

About the Role: The Claude Agentic Solutions team is Anthropic's forward-deployed engine , embedding directly with Fortune 500 enterprises to architect, build, and deploy production AI agents that transform how they work. We partner with companies that have decided AI is a strategic priority and need Anthropic alongside them to make it real.

You will own how Anthropic Solutions shows up in your industry segment , from the quality of individual engagements to the strategy that defines which problems we solve and for whom. This is fundamentally a technical leadership role: you set the bar for delivery, develop your team's vertical expertise, and ensure every engagement produces outcomes worth repeating.

Responsibilities:

Tech Leadership

• Lead and grow a team of Forward Deployed Engineers and embedded within your vertical.
• Provide active delivery oversight , review SOWs before signature, participate in architecture decisions on complex engagements, and serve as the escalation layer when delivery is at risk.
• Develop your team's vertical fluency: help FDEs understand the regulatory environment, data constraints, and stakeholder dynamics specific to your industry.
• Hire for your team: define role requirements, evaluate candidates, and make judgment calls on who will succeed in this specific context.
• Serve as the senior Anthropic presence in strategic customer conversations, including C-suite and board-level engagements where enterprise AI direction is being set.
• Run operating cadences , monthly engagement reviews, knowledge-sharing sessions, and escalation support for active engagements , while participating in or running client steering committees and unblocking delivery when at risk.

Use Case Library & Reusable Patterns

• Ensure the vertical use case library and reusable asset repository are maintained, prioritized, and mapped to business impact and implementation complexity.
• Systematically surface learnings from delivery back to product and research teams, identifying where customer environments reveal model limitations, platform gaps, or roadmap opportunities.
• Track emerging AI capabilities and assess their relevance for your vertical; bring forward new use case opportunities before clients ask for them.
• Partner with product teams to ensure delivery insights translate into platform improvements and roadmap decisions.

Commercial Leadership

• Partner with Marketing and Sales to develop vertical-specific GTM materials, reference cases, and positioning that reflect what actually wins in your industry.
• Own the qualified pipeline for your vertical , working with Sales and Account Executives to identify, shape, and progress enterprise AI opportunities from first conversation to signed SOW.
• Define how Anthropic goes to market with the Solutions offering in your vertical , including engagement structures, and partnership dynamics.
• Represent your vertical internally , bring the voice of your segment into product, and go-to-market discussions at the leadership level.

Requirements:

• 8+ years of experience spanning enterprise AI/ML delivery, management consulting (Big 3 / Big 4), or enterprise tech professional services , with at least 3 years in a senior leadership capacity owning commercial outcomes.
• Comfort operating in ambiguity and building processes where none exists , you've worked in early-stage, founder, or 0-to-1 environments, and can establish practices from scratch rather than executing within well-defined frameworks.
• Deep domain expertise in your vertical: you should be able to speak credibly with a CEO, CIO, and other executive SMEs.
• A track record of building and leading technical delivery teams in enterprise environments, ideally including embedded or forward-deployed engineers working at client sites.
• Demonstrated ability to carry pipeline and close complex enterprise deals , you understand procurement, know how to navigate large organizations, and can move opportunities forward.
• Strong technical fluency to review SOW scope, participate in architecture discussions, evaluate trade-offs, and judge whether a proposed approach is realistic , without being the one writing the code.
• Experience navigating regulated industry environments.
• Executive presence and communication skills: you can present to a board or run a difficult conversation.

Annual Compensation Range:

£255,000-£325,000 GBP

XML job scraping automation by YubHub

As a Data Scientist lead on the GeminiApp team, you are a key partner and co-creator in our product strategy while managing a growing team. You will be instrumental in building a uniquely proactive and powerful assistant by ensuring our strategic decisions are grounded in data.

Key responsibilities include: Partner with PM, engineering, and UX leadership to develop data-driven product strategies across various areas of GeminiApp Translate ambiguous questions into well-defined problems, design experiments, and analyze large complex datasets for insights Develop and implement novel, goal-oriented metrics Build and deploy statistical/ML models to understand our users, enhance product capabilities and personalize user experience Communicate findings & recommendations to stakeholders, including executives Champion data-driven culture by feeding user engagement insights back into models Collaborate with the GenAI team on model quality and feature adoption Act as a technical leader for a global team, guiding junior members on complex analyses and upholding best practices to ensure high-quality, impactful work

We are looking for a bias for action and a relentless drive to build something great. You are a resourceful and creative problem-solver, able to find innovative solutions in a fast-paced and dynamic environment. You are comfortable with ambiguity and thrive on a challenge.

Strong business acumen and a strategic mindset. You have a proven ability to connect data-driven insights to business impact and can effectively translate business needs into data science problems.

Deep technical expertise. You have a strong foundation in statistical modeling, machine learning, and experimental design. You are proficient in SQL and have hands-on experience with a programming language like Python or R.

Exceptional communication and presentation skills. You can articulate complex technical concepts to both technical and non-technical audiences, including executive leadership. You are a compelling storyteller who can use data to influence decisions.

A collaborative and influential partner. You have a track record of successfully collaborating with and influencing cross-functional teams, particularly engineering and product management, to drive data-informed decisions. You are ready to work in an environment with a shared roadmap that holds multiple teams accountable for both model quality and feature adoption.

A commitment to user trust and privacy. You understand the importance of telling users what data is being used and why, and you are committed to providing users with granular, easy-to-understand controls over their personal information.

You must meet the following minimum qualifications to

XML job scraping automation by YubHub

As a member of Medium’s Machine Learning & Insights team, you’ll partner closely with stakeholders across teams to help deepen our collective understanding of Medium’s members, writers, and business through data.

You'll work alongside our Principal Scientist, contributing methodological rigor to strategic initiatives while owning end-to-end research and model development in your domain.

This is a unique role for someone with a track record of solving big, ambiguous problems at the intersection of data, product, and business strategy.

You’ll do more than ivory-tower modeling; you’ll help us define what “content quality” looks like, design better experiments, and ship real product changes to users.

If you love both statistical rigor and real-world business impact, this might be the role for you!

Key Responsibilities

• Proactively identify valuable areas of investigation that help deepen our understanding of our members, writers, and overall business.

• Partner with diverse technical and non-technical stakeholders across the company to develop hypotheses and generate actionable insights in their domains.

• Work with executives at Medium, including our CEO, to model and present data insights and findings.

• Build and maintain statistical and predictive models about Medium’s business.

• Run research projects and investigations, small and large, for leadership and cross-functional partners.

• Develop and maintain quantitative models that support forecasting and strategic planning.

• Share knowledge with and mentor engineers and other stakeholders to improve their own analytics capabilities.

• Contribute to the broader data culture and ecosystem at Medium, helping to raise our data fluency as a team.

• Attend Medium’s twice-yearly, in-person offsites (hosted in locations around the U.S.).

Skills, Knowledge and Expertise

• You know your way around data. You have 4-6 years of experience as an in-house data scientist, with a proven track record of driving business impact through data.

• You're highly proficient in statistical programming with either Python or R, and you’re comfortable writing SQL for analytical queries. (Python skills are strongly preferred. Our team uses Python extensively, and we’ll be expecting candidates to demonstrate Python scripting skills during the interview process.)

• You have a track record of building, validating, and deploying predictive and statistical models that drove measurable business outcomes.

• You're a strong collaborator with an established history of cross-team and executive-level partnership.

• You care about quality writing, informed readership, and building a sustainable model for creators. Experience applying modeling techniques to problems unique to social platforms, subscription/membership businesses, or publishing is a plus.

• Experience with ML engineering practices, dbt, or data engineering is a plus! We're a small team, and the folks who do best are those who like to wear many hats.

Benefits

In addition to the new skills you'll pick up, here's what else you'll enjoy by working at Medium:

• Working with a fully distributed team: We’re fully remote and have teammates across the U.S. & France.

• Healthcare benefits covered at 100% for employees and 70% for dependents.

• Generous parental leave policy.

• Mental health support through Talkspace.

• Financial wellness support through Northstar.

• Stipends for co-working, professional development, wifi, and a one-time home office bonus.

• Unlimited PTO and standard company holidays.

• A discounted Medium membership!

XML job scraping automation by YubHub

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

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

XML job scraping automation by YubHub

The ideal candidate will possess a unique blend of business acumen and data science expertise. They will lead an innovative team focused on delivering data-driven insights and building tools for effective management of the marketplace dynamics.

Key responsibilities include:

• Leading and developing a high-performing geographically distributed team, fostering a culture of innovation, agility, growth, and collaboration.
• Driving crafts and analytical excellence on advanced data analysis, statistical modeling, experimentation, and metric frameworks.
• Serving as a key analytics partner to senior stakeholders, providing data-driven insights to guide key strategic decisions.
• Collaborating with teams including Strategic Finance, Data Engineering, Data Science, and Product Marketing in addition to other cross-functional teams.

Requirements include:

• A minimum of 10 years experience in advanced analytics, preferably in marketplace and/or travel industries with at least 4 years in people management roles.
• Experience in leading and motivating a geographically distributed team.
• Deep technical expertise in data analysis, experimentation, causal inference, and familiarity with LLMs.
• Robust business acumen, strategic thinking skills, and the ability to make informed judgments.
• Outstanding communication skills, capable of engaging with a variety of stakeholders and conveying complex concepts in an accessible manner.
• Exceptional stakeholder management skills, with a proven ability to collaborate and influence across functions.
• An agile, growth-minded approach, demonstrated through a history of driving projects from ideation to impact.

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.

XML job scraping automation by YubHub

We are seeking a highly motivated and talented Full-Stack Data Scientist to join our Identity Data Science team. As a Senior Data Scientist, you will play a key role in designing and implementing cutting-edge identity verification systems and defending against emerging threats.

The Ideal Candidate:

• Has a strong background in machine learning and statistical modeling
• Is experienced in working with large datasets and developing scalable data pipelines
• Has excellent communication and collaboration skills
• Is passionate about using data to drive business decisions

Responsibilities:

• Improve on industry standards in identity verification by leveraging biometrics, NFC chips, Apple/Google integrations, and other advancements in identity verification technologies
• Build high-performing statistical models for detecting identity fraud, such as computer vision models for identifying fake or tampered images, LLMs for surfacing suspicious user account attributes, or graph-based models for uncovering hidden clusters of bad actors
• Automate and optimize human-in-the-loop ML processes for classifying fraud and generating other labels of interest for model training and evaluation
• Deploy a real-time anomaly detection system for quickly identifying emerging threats across regions, cohorts, and platforms
• Design intelligent sampling jobs for estimating rare events prevalence and other hard-to-measure metrics like recall and false positive/negative rates

A Typical Day:

• AI/ML: Build and deploy production AI/ML models for detecting identity fraud and improving Airbnb’s identity verification systems (feature engineering, model development + evaluation, threshold selection, error analysis, model lifecycle management)
• Inference: Conduct experiments and lead quantitative analyses for measuring impact, surfacing critical gaps, and identifying opportunities for improvement
• Optimization: Develop methodologies and frameworks for analyzing the tradeoffs associated with new interventions and propose strategies for optimizing impact
• 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 opportunities to improve and scale our identity verification processes and defenses

Required Qualifications:

• 5+ years of industry experience in a quantitative analysis role with a Master’s degree in a quantitative field (computer science, statistics, economics, etc.), or 2+ years of experience with a Ph.D.
• State-of-the-art knowledge of AI/ML models
• Strong 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.

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.

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

XML job scraping automation by YubHub

You'll own the technical strategy for turning raw data center capacity into reliable, efficient compute, working across power topology, workload scheduling, and real-time telemetry to push utilization as close to the physical envelope as possible while maintaining our availability commitments.

Key responsibilities include:

• Building models that forecast consumption across electrical and mechanical subsystems, informing capacity planning, energy procurement, oversubscription targets and risks, including statistical modeling of cluster utilization, workload profiles, and failure modes.

• Designing IT/OT interfaces that bridge compute orchestration with facility controls, enabling real-time telemetry across accelerator hardware, power distribution, cooling, and schedulers.

• Building and operating load management systems that use power and cooling topology to enable load management and power/thermal-aware placement to maximize throughput while meeting SLOs.

• Partnering with data center providers to drive design optimizations and hold them accountable to SLA-grade performance standards, providing technical diligence on partner architectures.

In this role, you'll need to have deep knowledge of data center power distribution and cooling architectures, and how they interact with IT load profiles. Experience with reliability engineering, SLA development, and failure-mode analysis is also essential.

Additionally, proficiency in statistical modeling and simulation for infrastructure capacity or power utilization, familiarity with SCADA/BMS/EPMS, telemetry pipelines, and control systems, and exposure to accelerator deployments and their power management interfaces are highly desirable.

This is a challenging and rewarding role that requires a unique blend of technical expertise, business acumen, and collaboration skills. If you're passionate about data center infrastructure, AI, and sustainability, we encourage you to apply.

XML job scraping automation by YubHub

This role is ideal for someone who thrives in a dynamic environment, enjoys tackling ambiguous problems, and wants to influence our product roadmap directly. The ideal candidate will have a strong background in data analysis, statistical modeling, and machine learning, with a proven track record of delivering high-quality results in a fast-paced environment.

Key responsibilities include:

• Defining key metrics, understanding in-depth the performance of our AI products, and identifying opportunities for improvement.
• Designing and analyzing experiments (A/B tests, causal inference) to validate hypotheses and guide product decisions.
• Designing and implementing end-to-end data science projects, from data collection and preprocessing to model building, evaluation, and deployment.
• Evaluating model performance in training and production, identifying edge cases, and developing frameworks to measure user satisfaction.
• Working with engineering to ensure data quality and accessibility for analytics and ML.

Nice-to-have skills include familiarity with DBT or similar tools, and experience working on customer-facing products, enterprise products, and with the science team.

By joining our team, you will have the opportunity to work on exciting projects, collaborate with talented individuals, and contribute to the development of cutting-edge AI solutions.

XML job scraping automation by YubHub

This position requires strong expertise in commercial credit risk, advanced analytical skills, and the ability to translate complex insights into actionable strategies.

Job Responsibilities:

• Lead the development of best in class data-driven credit policies and business strategies: Leverage external (e.g. banking, tax, financial, credit bureau and other data) and internal performance data to develop credit policies (e.g. underwriting criteria, dynamic credit limit programs, etc.). Minimize credit loss by developing and implementing appropriate processes and procedures to identify and mitigate high risk customers. Design and implement data-driven strategies for customer management, including policies for credit line increases, decreases, and line reinstatements. Work closely with the sales and business development teams to support business growth strategies that preserve effective underwriting and ensure the appropriate application of Jeeves’ credit policy.

• Deliver advancements in data and analytics: Contribute to the development of a data and analytics framework to improve processing, underwriting, tracking, risk management, and reporting procedures. Design and execute data driven analyses and tracking procedures to enhance insights on credit risk for individual, and the portfolio of companies. Leverage predictive statistical methodologies (e.g., linear/logistic regression, segmentation analysis) to draw insights and develop business strategies. Partner with data scientists to build and leverage credit risk models to optimize credit policies and improve business performance.

• Improve credit monitoring and governance: Define key performance indicators (KPIs) for credit portfolios; build dashboards to monitor portfolio health and strategy performance. Develop and implement improvements to credit portfolio monitoring, client review tracking, management reporting, and customer risk rating assignments. Partner with product, engineering, and finance teams to create and maintain risk adjusted returns framework, including loss forecasting, and execute credit policies and features Implement risk metric monitoring and develop risk mitigation strategies.

• Provide guidance and mentorship to junior analysts on the team, fostering a culture of rigorous analysis and innovation.

Required Qualifications:

• 4+ years in analytics within credit risk management.
• Fluent in English.
• Excellent written and verbal communication skills.
• Intellectual curiosity.
• Experience in commercial credit risk, specifically within credit cards, payments, lending, or related industries.
• Proven ability to translate complex data and model results into actionable business strategies.
• Advanced proficiency in SQL and Excel.
• Experience using Python or R for data analysis and statistical modeling (regression, clustering, etc.).

Preferred Qualifications:

• Experience in architecting, implementing, and interpreting risk/scoring models in conjunction with data science teams.
• Experience in high growth startups.
• Experience with visualization tools such as Tableau.
• Experience building complex financial products and models.

XML job scraping automation by YubHub

Key responsibilities include:

• Leading the development of best-in-class data-driven credit policies and business strategies
• Delivering advancements in data and analytics
• Improving credit monitoring and governance

This position requires strong expertise in commercial credit risk, advanced analytical skills, and the ability to translate complex insights into actionable strategies.

You will work closely with the sales and business development teams to support business growth strategies that preserve effective underwriting and ensure the appropriate application of Jeeves' credit policy.

You will also contribute to the development of a data and analytics framework to improve processing, underwriting, tracking, risk management, and reporting procedures.

In addition, you will define key performance indicators (KPIs) for credit portfolios; build dashboards to monitor portfolio health and strategy performance.

You will partner with data scientists to build and leverage credit risk models to optimize credit policies and improve business performance.

You will provide guidance and mentorship to junior analysts on the team, fostering a culture of rigorous analysis and innovation.

Required qualifications include:

• 4+ years in analytics within credit risk management
• Fluent in English
• Excellent written and verbal communication skills
• Intellectual curiosity
• Experience in commercial credit risk, specifically within credit cards, payments, lending, or related industries
• Proven ability to translate complex data and model results into actionable business strategies
• Advanced proficiency in SQL and Excel
• Experience using Python or R for data analysis and statistical modeling (regression, clustering, etc.)

Preferred qualifications include:

• Experience in architecting, implementing, and interpreting risk/scoring models in conjunction with data science teams
• Experience in high growth startups preferred
• Experience with visualization tools such as Tableau
• Experience building complex financial products and models

XML job scraping automation by YubHub

Key responsibilities include: Developing best-in-class data-driven credit policies and business strategies, Delivering advancements in data and analytics, Improving credit monitoring and governance.

This role requires strong technical proficiency, a solid foundation in credit risk analytics, and the ability to turn complex data into actionable business strategies.

Location: This role is based out of Mexico City, Mexico, and is a full-time hybrid position where it is required to come into our office in Roma Norte (2–3 days/week).

#LI-HYBRID

XML job scraping automation by YubHub

As a Credit Business Analyst, you will contribute to advancing our data and analytics framework, leveraging statistical methods and credit risk models to generate insights and drive strategy. You will also enhance credit monitoring and governance by building dashboards, improving reporting processes, and refining risk rating practices.

This role requires strong technical proficiency, a solid foundation in credit risk analytics, and the ability to turn complex data into actionable business strategies.

XML job scraping automation by YubHub

You will contribute to advancing our data and analytics framework, leveraging statistical methods and credit risk models to generate insights and drive strategy. Additionally, you will enhance credit monitoring and governance by building dashboards, improving reporting processes, and refining risk rating practices.

This role requires strong technical proficiency, a solid foundation in credit risk analytics, and the ability to turn complex data into actionable business strategies.

Job Responsibilities:

• Develop best in class data-driven credit policies and business strategies: Leverage external (e.g. banking, tax, financial, credit bureau and other data) and internal performance data to develop credit policies (e.g. underwriting criteria, dynamic credit limit programs, etc.). Minimize credit loss by developing and implementing appropriate processes and procedures to identify and mitigate high risk customers.

• Deliver advancements in data and analytics: Contribute to the development of a data and analytics framework to improve processing, underwriting, tracking, risk management, and reporting procedures. Design and execute data driven analyses and tracking procedures to enhance insights on credit risk for individual, and the portfolio of companies.

• Improve credit monitoring and governance: Build dashboards to monitor portfolio health and strategy performance. Develop and implement improvements to credit portfolio monitoring, client review tracking, management reporting, and customer risk rating assignments

Required Qualifications:

• 2+ years in analytics within credit risk management.

• Fluent in English.

• Excellent written and verbal communication skills.

• Intellectual curiosity.

• Ability to translate complex data and model results into actionable business strategies.

• Proficiency in SQL and Excel.

• Experience using Python or R for data analysis and statistical modeling (regression, clustering, etc.)

XML job scraping automation by YubHub

Job Summary

We're seeking a highly skilled Quantitative Researcher to join our team at Fuse Energy. As a Quantitative Researcher, you will develop and implement quantitative models to support hedging strategies for retail energy supply, analyze market data to optimize the performance of physical assets, and conduct statistical and scenario-based modeling to inform trading and risk management decisions.

Responsibilities

• Develop and implement quantitative models to support hedging strategies for retail energy supply
• Analyze market data to optimize the performance of physical assets such as solar and wind farms
• Conduct statistical and scenario-based modeling to inform trading and risk management decisions
• Monitor power markets, pricing trends, and regulatory developments to identify risks and opportunities
• Collaborate with engineering and operations teams to integrate models into day-to-day decision-making

Requirements

• 1+ years of experience in quantitative modeling, data analysis, or a related role in energy, trading, or analytics
• Strong proficiency in Python, with experience using libraries for data analysis, modeling, and visualization
• Solid understanding of statistical modeling, optimization, and analysis techniques
• Experience working with time series or market data, preferably in energy or commodities markets
• Strong problem-solving skills and the ability to translate complex data into actionable insights

Benefits

• Competitive salary and an equity sign-on bonus
• Biannual bonus scheme
• Fully expensed tech to match your needs
• Paid annual leave
• Breakfast and dinner allowance for office-based employees

XML job scraping automation by YubHub

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Interpretability team:

When you see what modern language models are capable of, do you wonder, 'How do these things work? How can we trust them?'

The Interpretability team's mission is to reverse engineer how trained models work, and Interpretability research is one of Anthropic's core research bets on AI safety. We believe that a mechanistic understanding is the most robust way to make advanced systems safe.

People mean many different things by 'interpretability'. We're focused on mechanistic interpretability, which aims to discover how neural network parameters map to meaningful algorithms. Some useful analogies might be to think of us as trying to do 'biology' or 'neuroscience' of neural networks, or as treating neural networks as binary computer programs we're trying to 'reverse engineer'.

About the role:

As a manager on the Interpretability team, you'll support a team of expert researchers and engineers who are trying to understand at a deep, mechanistic level, how modern large language models work internally.

Few things can accelerate this work more than great managers. Your work as manager will be critical in making sure that our fast-growing team is able to meet its ambitious safety research goals over the coming years. In this role, you will partner closely with an individual contributor research lead to drive the team's success, translating cutting-edge research ideas into tangible goals and overseeing their execution. You will manage team execution, careers and performance, facilitate relationships within and across teams, and drive the hiring pipeline.

Responsibilities:

• Partner with a research lead on direction, project planning and execution, hiring, and people development
• Set and maintain a high bar for execution speed and quality, including identifying improvements to processes that help the team operate effectively
• Coach and support team members to have more impact and develop in their careers
• Drive the team's recruiting efforts, including hiring planning, process improvements, and sourcing and closing
• Help identify and support opportunities for collaboration with other teams across Anthropic
• Communicate team updates and results to other teams and leadership
• Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

• Are an experienced manager (minimum 2-5 years) with a track record of effectively leading highly technical research and/or engineering teams
• Have a background in machine learning, AI, or a related technical field
• Actively enjoy people management and are experienced with coaching and mentorship, performance evaluation, career development, and hiring for technical roles
• Have strong project management skills, including prioritization and cross-functional coordination and collaboration
• Have managed technical teams through periods of ambiguity and change
• Are a quick learner, capable of understanding and contributing to discussions on complex technical topics and are motivated to learn about our research
• Are a strong communicator

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

What you'll do

• Work directly with the search team to understand their offerings/domain and create data science products and solutions to solve for their use cases.
• Partner with our team lead for Recommendations and Personalization to help surface uniquely personalized content to each player.
• Collaborate closely with Machine Learning Engineers and Machine Learning Operations to deploy and monitor models.
• Coach and mentor other Data Scientists and Senior Data Scientists on the team, ensuring technical best practices and guiding stakeholder communication.
• Ensure sound methodology and experimental design, and guide the direction of each project from start to finish.
• Effectively communicate data science projects and products to stakeholders, including those with non-data science backgrounds.

What you need

• 6+ years of professional experience manipulating data sets, building statistical models and productizing models along with experience applying Data Science methodologies to real-world problems.
• Strong expertise in building, deploying, and testing search algorithms.
• Experience with deep learning.
• Experienced in analyzing extremely complex, multi-dimensional data sets with a variety of tools.
• Superior verbal, visual, and written communication skills to educate and work with cross-functional teams.
• Experience using Python, SQL, Hive, PyTorch/TensorFlow, and AWS/GCP.
• Experience guiding and mentoring more junior Data Scientists.

XML job scraping automation by YubHub