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

Responsibilities:

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

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

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

• Labor markets and the future of work

• Productivity and task transformation

• Economic inequality and displacement

• Industry-specific disruption and adaptation

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

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

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

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

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

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

You May Be a Good Fit If You Have:

• PhD in Economics

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

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

• Labor market analysis and occupational change

• Task-based approaches to technological transformation

• Large-scale data analysis and econometric methods

• Large language models for social science research

• Policy-relevant economic research

• Experimental and quasi-experimental methods for causal inference

• Macroeconomic modeling and time series forecasting

• Agent-based modeling or large-scale simulation

• Technical skills including:

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

• Experience working with novel datasets and measurement systems

• Comfort learning new technical tools and frameworks

• Demonstrated ability to:

• Lead complex research projects from conception to publication

• Communicate technical findings to diverse audiences

• Build relationships across academic, policy, and industry communities

• Strong interest in ensuring AI development benefits humanity

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

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As a Software Engineer on the Research Data Platform team, you will:

• Build and operate data pipelines that extract data from research training runs and land it in storage systems that are easy and fast to query
• Work closely with researchers to design and build APIs, libraries, and web interfaces that support data management, exploration, and analysis
• Develop dataset management, data cataloging, and provenance tooling that researchers use in their day-to-day work
• Embed with research teams to understand their workflows, identify high-leverage tooling opportunities, and ship solutions quickly
• Collaborate with adjacent teams to build on existing systems rather than reinventing them

We do not require prior ML or AI training experience. If you enjoy working closely with technical users, learning new domains quickly, and building tools people actually want to use, you'll pick up the research context fast.

Strong candidates may also have experience with large-scale ETL, columnar storage formats, and query engines (e.g., Spark, BigQuery, DuckDB, Parquet), high-volume time series data , ingestion, storage, and efficient querying, data cataloging, lineage, or metadata management systems, or ML experiment tracking or metrics platforms.

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You will partner closely with Sales and Data Science teams to identify important questions and answer them with data. You will design, implement and launch data science solutions using Python and R to empower data-driven decisions and products at scale.

You will create analyses that generate insights for business teams. You will design, analyze, and interpret the results of experiments. You will build ETL data transformation pipelines in SQL.

The ideal candidate will have a Master's degree or foreign equivalent in Computer Science, Business Analytics, Statistics, or a related quantitative field, plus 3 years of experience in a Data Scientist or related occupation.

We offer a competitive salary range of $192,000 - $288,000/yr. This is a full-time position with 40 hours/week and 50% telecommuting permitted.

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As a software engineer on this team, you will:

• Build and operate data pipelines that extract data from research training runs and land it in storage systems that are easy and fast to query
• Work closely with researchers to design and build APIs, libraries, and web interfaces that support data management, exploration, and analysis
• Develop dataset management, data cataloging, and provenance tooling that researchers use in their day-to-day work
• Embed with research teams to understand their workflows, identify high-leverage tooling opportunities, and ship solutions quickly
• Collaborate with adjacent teams to build on existing systems rather than reinventing them

You may be a good fit if you have significant software engineering experience, particularly building data-intensive applications or internal tooling. You should enjoy working directly with users, gathering requirements iteratively, and shipping things that get adopted. You should also be results-oriented, with a bias towards flexibility and impact.

Strong candidates may also have experience with large-scale ETL, columnar storage formats, and query engines, high-volume time series data, data cataloging, lineage, or metadata management systems, ML experiment tracking or metrics platforms, complex data visualization, and full-stack web application development.

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Our team operates with a flat organisational structure, and leadership is given to those who show initiative and consistently deliver excellence. We value strong communication skills, and all employees are expected to contribute directly to the company's mission.

You'll be working with a range of technologies, including Go, Rust, Scala, Prometheus, Grafana, OpenTelemetry, VictoriaMetrics, and ClickHouse. Experience with Kafka, Redis, and large-scale time series databases is also essential.

In this role, you'll own the reliability, scalability, and performance of the observability stack end-to-end. You'll partner with infrastructure and product teams to deeply integrate observability into our internal platforms.

We offer a competitive salary of $180,000 - $440,000 USD, plus equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

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The MaaS Data Science team is central to all money movements, embedded finance, and platform solutions for our biggest and most complex customers. The two roles available are in Embedded Finance (Capital, Issuing) and Connect (Stripe's solution and growth suite for platforms and marketplaces).

The Growth Data Science team helps businesses on Stripe get started both quickly and effectively. We work closely with Growth product and engineering leads to optimize every step of the user journey, from awareness and acquisition, through product adoption, to usage growth and retention.

Responsibilities:

• Drive the roadmap and priorities for your team, and work with many Stripe leaders across the company to enhance our ability to be data driven.
• Collaborate with stakeholders across the organization such as engineering, analytics, operations, finance, and marketing.
• Lead and manage processes to help the team do its best work and engage effectively with the rest of Stripe.
• Manage a high-performing team of data scientists, supporting them to achieve a high level of technical excellence and advance in their careers.
• Recruit and onboard great data scientists, in collaboration with Stripe’s recruiting team.
• Contribute to broad data science initiatives as a member of Stripe’s data science management team.

Requirements:

• PhD or MS or BS in a quantitative field (e.g., Statistics, Operations Research, Economics, Computer Science, Engineering).
• At least 3 years of direct management experience leading data science or ML teams, and 10 years of overall data science experience.
• Demonstrated expertise in designing metrics and guiding business decisions with data.
• Technical expertise to drive clarity with staff and senior scientists about architecture and strategic modeling decisions.
• Managed teams that have built and shipped machine learning systems and data products at scale, and have hands-on experience with challenging problems.
• Work very well cross-functionally, and are able to think rigorously and make hard decisions and tradeoffs.
• Clear and persuasive communication skills in writing and verbally.
• Thrive on a high level of autonomy and responsibility.
• Foster a healthy, inclusive, challenging, and supportive work environment.

Preferred Requirements:

• Comfortable working with geographically distributed teams.
• Expertise in time series forecasting, predictive modeling, or optimization.
• Expertise in data design and building scalable data architectures.

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You will work hands-on on the design, build, and operationalisation of modern data and analytics solutions. You will contribute across the full lifecycle – from data ingestion and transformation to analytics, machine learning, and production deployment. You will collaborate closely with data engineers, architects, data scientists, and business stakeholders to deliver scalable, reliable, and value-driven data solutions in complex client environments.

Responsibilities

• Apply data science and machine learning techniques to real-world business problems
• Work with structured and semi-structured data in data lakes, lakehouses, and data warehouses
• Develop and optimise data transformations for analytical and machine learning workloads
• Support the productionisation of data and ML solutions, including monitoring and optimisation

Requirements

• 3–5 years of experience in data engineering, data science, or analytics
• Hands-on experience delivering data and analytics solutions in project-based or client environments
• Strong problem-solving skills and a pragmatic, delivery-oriented mindset

Data Engineering Foundations

• Experience building end-to-end data pipelines (ingestion, transformation, storage)
• Solid understanding of data modelling, data transformations, and feature engineering
• Familiarity with cloud-based data platforms, such as Azure, AWS, or GCP

Applied Data Science & Analytics

• Experience applying statistical analysis and machine learning techniques
• Strong programming skills in Python
• Very good SQL skills and experience working with relational databases

Nice to have

• Experience with streaming technologies (e.g. Kafka, Azure Event Hubs)
• Exposure to GenAI, NLP, time series, or advanced analytics use cases
• Experience with NoSQL databases (e.g. MongoDB, Cosmos DB)

Language & Mobility

• Very good English skills
• Willingness to travel for project-related work

Benefits

Join our growing Data & Analytics practice and make a difference. In this practice you will be utilizing the most innovative technological solutions in modern data ecosystem. In this role you’ll be able to see your own ideas transform into breakthrough results in the areas of Data & Analytics strategy, Data Management & Governance, Data Platforms & Engineering, Analytics & Data Science.

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At Fuse Energy, we're on a mission to deliver a terawatt of renewable energy - fast. We're combining first-principles thinking with cutting-edge technology to build a radically better energy system.

As the Head of Engineering (AI) at Fuse Energy, you will lead the development and integration of AI across our platform—from intelligent forecasting models and optimization algorithms to personalized customer experiences and internal automation.

Responsibilities

• Own the AI engineering roadmap and lead the development of AI-first features
• Productionize ML models, ensuring scalability, performance, and observability
• Design the infrastructure for deploying and maintaining ML systems in production (e.g., MLOps, CI/CD for ML, model versioning)
• Build systems that integrate AI into key parts of our stack, such as:
• Forecasting customer demand and renewable generation
• Dynamic pricing and energy trading algorithms
• Intelligent alerts and personalized customer features
• Work closely with product and engineering leadership to identify high-impact AI opportunities
• Build and lead a high-performing team of AI engineers

Requirements

• Strong software engineering background with 5+ years of experience, including at least 2 years leading AI/ML engineering teams
• Deep experience deploying ML models into production environments
• Proficiency in designing scalable data pipelines and real-time inference systems
• Understanding of modern ML tooling and frameworks (e.g., PyTorch, TensorFlow, MLflow, AWS SageMaker)
• Strong cross-functional collaboration skills, particularly with data science and product teams
• Clear communication and an ability to prioritize for both experimentation and reliability

Bonus

• Familiarity with optimization, time series modeling, or forecasting
• Experience with large language models (LLMs), RAG, or generative AI in production
• Background in MLOps or AI infrastructure at scale

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

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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

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Our dynamic organisation allows you to work across topics and bring in your ideas, experiences, creativity, and goal orientation. Are you ready?

As a Consultant/Senior Consultant in the Data Engineering & Data Science field, you will work hands-on on the conception, development, and implementation of modern data and analytics solutions. You will support the entire project lifecycle - from data intake and transformation to analytics and machine learning to productive operation.

You will work closely with data engineers, architects, data scientists, and subject matter experts to implement scalable, reliable, and value-adding solutions in complex customer environments.

Your Tasks

• Apply data science methods (machine learning, deep learning, GenAI) to solve concrete business questions
• Work with structured and semi-structured data in data lakes, lakehouses, and data warehouses
• Set up data pipelines for analytical workloads
• Support the productive implementation of data and ML solutions, including monitoring and optimisation

What You Bring - Required

• At least 3 years of relevant professional experience in the field of data engineering, data science, or analytics
• Hands-on experience in implementing data and analytics solutions in (customer) projects
• Strong problem-solving skills and a pragmatic, implementation-oriented way of working

Data Engineering Fundamentals

• Experience in setting up data pipelines (ingestion, transformation, storage)
• Solid understanding of data modeling, data transformations, and feature engineering
• Experience with cloud-based data platforms, such as:

1. Azure, AWS, or GCP
2. Databricks, Snowflake, BigQuery, Azure Synapse/Microsoft Fabric

• Knowledge of CI/CD concepts and production-ready deployments

Applied Data Science & Analytics

• Experience in applying GenAI, deep learning, and machine learning procedures as well as statistical analyses
• Very good programming skills in Python
• Very good SQL skills and experience with relational databases
• Experience in deploying and productively using ML models
• Ability to translate analytical results into business-relevant insights
• Bachelor's or master's degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience

Nice to Have

• Experience with:

1. Streaming technologies (e.g. Kafka, Azure Event Hubs)
2. Time series analysis, NLP applications, or system modeling
3. NoSQL databases (e.g. MongoDB, Cosmos DB)
4. Docker and Kubernetes
5. Data visualization tools like Power BI, Tableau
6. Cloud or architecture certifications

Language & Mobility (Germany)

• Fluent German skills (at least C1) for customer communication in the German-speaking market
• Very good English skills
• Project-related travel readiness

Your Team

You will become part of our growing Data & Analytics teams. In this area, you will work with modern technologies in modern data ecosystems. You have the opportunity to turn your own ideas into results - in the areas of Data & Analytics Strategy, Data Management & Governance, Data Platforms & Engineering, and Analytics & Data Science.

About Infosys Consulting

You will become an employee of a globally renowned management consulting firm at the forefront of technological innovation and industrial transformation. We work across industries with leading companies. Our culture is inclusive and entrepreneurial. As a mid-sized consulting firm embedded in the size of Infosys, we can support our customers worldwide and throughout the entire transformation process in a partnership-like manner.

Our values IC-LIFE - Inclusion, Equity & Diversity, Client, Leadership, Integrity, Fairness, and Excellence - form our compass of values. Further information can be found on our career website.

In Europe, we are awarded by the Financial Times and Forbes as one of the leading consulting firms. Infosys is ranked among the top employers in Germany 2023 and has been certified by the Top Employers Institute for outstanding working conditions in Europe for five consecutive years.

We offer a market-leading salary, attractive additional benefits, and excellent opportunities for further education and development. Have you become curious? Then we look forward to your application - apply now!

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You will work hands-on on the design, build, and operationalisation of modern data and analytics solutions. You will contribute across the full lifecycle – from data ingestion and transformation to analytics, machine learning, and production deployment. You will collaborate closely with data engineers, architects, data scientists, and business stakeholders to deliver scalable, reliable, and value-driven data solutions in complex client environments.

Your role will include:

• Applying data science and machine learning techniques to real-world business problems
• Working with structured and semi-structured data in data lakes, lakehouses, and data warehouses
• Developing and optimising data transformations for analytical and machine learning workloads
• Supporting the productionisation of data and ML solutions, including monitoring and optimisation

Requirements

• 3–5 years of experience in data engineering, data science, or analytics
• Hands-on experience delivering data and analytics solutions in project-based or client environments
• Strong problem-solving skills and a pragmatic, delivery-oriented mindset

Data Engineering Foundations

• Experience building end-to-end data pipelines (ingestion, transformation, storage)
• Solid understanding of data modelling, data transformations, and feature engineering
• Familiarity with cloud-based data platforms, such as Azure, AWS, or GCP
• Understanding of CI/CD concepts and production-grade deployments

Applied Data Science & Analytics

• Experience applying statistical analysis and machine learning techniques
• Strong programming skills in Python
• Very good SQL skills and experience working with relational databases
• Experience deploying or supporting ML models in production environments
• Ability to translate analytical results into business-relevant insights
• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field or equivalent practical experience

Nice to have

• Experience with streaming technologies (e.g. Kafka, Azure Event Hubs)
• Exposure to GenAI, NLP, time series, or advanced analytics use cases
• Experience with NoSQL databases (e.g. MongoDB, Cosmos DB)
• Familiarity with Docker and Kubernetes
• Experience with data visualisation tools (e.g. Power BI, Tableau)
• Cloud or data-related certifications

Language & Mobility

• Very good English skills
• Willingness to travel for project-related work

Benefits

Join our growing Data & Analytics practice and make a difference. In this practice you will be utilizing the most innovative technological solutions in modern data ecosystem. In this role you’ll be able to see your own ideas transform into breakthrough results in the areas of Data & Analytics strategy, Data Management & Governance, Data Platforms & Engineering, Analytics & Data Science.

About Infosys Consulting

Be part of a globally renowned management consulting firm on the front-line of industry disruption and at the cutting edge of technology. We work with market leading brands across sectors. Our culture is inclusive and entrepreneurial. Being a mid-size consultancy within the scale of Infosys gives us the global reach to partner with our clients throughout their transformation journey.

Our core values, IC-LIFE, form a common code that helps us move forward. IC-LIFE stands for Inclusion, Equity and Diversity, Client, Leadership, Integrity, Fairness, and Excellence. To learn more about Infosys Consulting and our values, please visit our careers page.

Within Europe, we are recognized as one of the UK’s top firms by the Financial Times and Forbes due to our client innovations, our cultural diversity and dedicated training and career paths. Infosys is on the Germany’s top employers list for 2023. Management Consulting Magazine named us on their list of Best Firms to Work for. Furthermore, Infosys has been recognized by the Top Employers Institute, a global certification company, for its exceptional standards in employee conditions across Europe for five years in a row.

We offer industry-leading compensation and benefits, along with top training and development opportunities so that you can grow your career and achieve your personal ambitions. Curious to learn more? We’d love to hear from you.... Apply today!

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The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation 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.

About the Role

We’re building the observability product for OpenAI—from scalable infrastructure to a rich, AI-powered UI. Our systems ingest over petabytes of logs and billions of time series metrics across our fleet. We're now layering intelligence on top—think agents that summarize SEVs, auto-generate dashboards, or help engineers debug through notebook-like UIs.

What You’ll Do

• Own core observability infrastructure, including distributed logging, time series, and trace storage

• Build AI-native tools that help engineers detect, understand, and resolve issues autonomously.

• Contribute to UI experiences like dashboards, notebooking, or interactive debugging

• Collaborate closely with engineers, researchers, user ops, and other teams across the company to build the next generation observability product

You Might Be a Fit If You:

• Have operated large-scale distributed systems in production. (especially logging systems or some other time series databases)

• Thrive in ambiguous environments and roll up your sleeves to solve unscoped problems.

• Have full-stack chops or product sensibilities—you're excited to build real tools people use.

• Have strong fundamentals in systems, networking, and cloud infra (Kubernetes, AWS, etc).

• Bonus: built or contributed to observability systems (e.g. Prometheus, OpenTelemetry, etc).

Why This Team

• We’re both an infra and product team—building a real AI application for internal use.

• Your work will directly power the reliability of GPT-based products at massive scale.

• You'll help define what 'AI-powered observability' looks like at one of the world’s most advanced AI labs.

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.

Additional Information

For additional information, please see [OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement](https://cdn.openai.com/policies/eeo-policy-statement.pdf).

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

To notify OpenAI that you believe this job posting is non-compliant, please submit a report through [this form](https://form.asana.com/?d=57018692298241&k=5MqR40fZd7jlxVUh5J-UeA). No response will be provided to inquiries unrelated to job posting compliance.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this [link](https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg&d=57018692298241).

[OpenAI Global Applicant Privacy Policy](https://cdn.openai.com/policies/global-employee-and-contractor-privacy-policy.pdf)

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Location

San Francisco

Employment Type

Full time

Department

Data Science

Compensation

• $440K – $490K • Offers Equity

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

Benefits

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

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

• 401(k) retirement plan with employer match

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

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

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

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

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

• Relocation support for eligible employees

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

About the Team

The Strategic Finance team at OpenAI plays a critical role in shaping the company’s long-term trajectory. We partner closely with Product, Engineering, and Go-to-Market teams to inform high-stakes decisions through rigorous data science and economic modeling.

As part of our expanding Data Science function, we’re building a best-in-class forecasting capability to drive real-time, data-driven decision-making across user growth, revenue, compute infrastructure, and more.

We are developing a scalable forecasting platform to help us understand and anticipate business dynamics in an increasingly complex, usage-based world. Our models are foundational to planning, pricing, operational efficiency, and growth strategy — supporting key investment decisions and unlocking OpenAI’s full potential.

About the Role

We’re looking for a hands-on technical leader to build and lead a small but mighty team of applied data scientists and ML engineers to develop forecasting capabilities and platforms from the ground up.

Your team will be responsible for building and scaling robust, interpretable, and production-ready forecasting systems. These models will power critical business decisions by predicting core metrics such as DAU/WAU, revenue, compute consumption, and profitability.

You will work closely with Strategic Finance leaders to integrate these scalable forecasting capabilities into their operational rhythms and collaborate with the Growth team to forecast key KPIs.

This is a highly cross-functional role that requires technical excellence, strong product intuition, and business acumen. You’ll collaborate with various partners to operationalize forecasting insights, influence company-wide strategy, and build foundational forecasting capabilities at OpenAI.

Responsibilities

• Build and manage a world-class team of applied data scientists and ML engineers to develop forecasting platforms at scale.

• Design and own the roadmap for the forecasting platform in partnership with cross-functional stakeholders.

• Collaborate closely with Strategic Finance teams to ensure forecasts are well integrated into planning processes, and executive decision-making.

• Work closely with cross-functional partners to help them adopt scientific, automated forecasting solutions.

• Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development and prototyping, experimentation, deployment, monitoring, and explainability.

• Research and evaluate emerging tools and techniques in the forecasting space.

• Translate technical outputs into business-aligned recommendations and decision frameworks.

Requirements

• Extensive experience building production-ready models for time series applications.

• Proven track record of building adjustable and explainable forecast models for multiple planning cycles.

• 10+ years of applied data science & engineering experience, with deep hands-on expertise in forecasting and predictive modeling.

• Demonstrated experience with model monitoring, debugging, and long-term maintenance in production environments.

• Excellent communication and storytelling skills — able to simplify complexity and influence executive stakeholders.

• Team leadership experience with a track record of building high-performing, engaged teams.

• An advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research).

• A self-directed, intellectually curious mindset and comfort leading ambiguous projects from 0→1.

• The ability to thrive in a dynamic environment — flexible, resourceful, and willing to do what it takes

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San Francisco

Employment Type

Full time

Department

Data Science

Compensation

• $347K – $490K • Offers Equity

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

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

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

• 401(k) retirement plan with employer match

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

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

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

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

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

• Relocation support for eligible employees

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

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

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

About the Team

The Strategic Finance team at OpenAI plays a critical role in shaping the company’s long-term trajectory. We partner closely with Product, Engineering, and Go-to-Market teams to inform high-stakes decisions through rigorous data science and economic modeling.

As part of our expanding Data Science function, we’re building a best-in-class forecasting capability to drive real-time, data-driven decision-making across user growth, revenue, compute infrastructure, and more.

We are developing a scalable forecasting platform to help us understand and anticipate business dynamics in an increasingly complex, usage-based world. Our models are foundational to planning, pricing, operational efficiency, and growth strategy — supporting key investment decisions and unlocking OpenAI’s full potential.

About the Role

We’re looking for a hands-on technical leader to build and lead a small but mighty team of applied data scientists and ML engineers to develop forecasting capabilities and platforms from the ground up.

Your team will be responsible for building and scaling robust, interpretable, and production-ready forecasting systems. These models will power critical business decisions by predicting core metrics such as DAU/WAU, revenue, compute consumption, and profitability.

You will work closely with Strategic Finance leaders to integrate these scalable forecasting capabilities into their operational rhythms and collaborate with the Growth team to forecast key KPIs.

This is a highly cross-functional role that requires technical excellence, strong product intuition, and business acumen. You’ll collaborate with various partners to operationalize forecasting insights, influence company-wide strategy, and build foundational forecasting capabilities at OpenAI.

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

In this role, you will:

• Build and manage a world-class team of applied data scientists and ML engineers to develop forecasting platforms at scale.
• Design and own the roadmap for the forecasting platform in partnership with cross-functional stakeholders.
• Collaborate closely with Strategic Finance teams to ensure forecasts are well integrated into planning processes, and executive decision-making.
• Work closely with cross-functional partners to help them adopt scientific, automated forecasting solutions.
• Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development and prototyping, experimentation, deployment, monitoring, and explainability.
• Research and evaluate emerging tools and techniques in the forecasting space.
• Translate technical outputs into business-aligned recommendations and decision frameworks.

You might thrive in this role if you have:

• Extensive experience building production-ready models for time series applications.
• Proven track record of building adjustable and explainable forecast models for multiple planning cycles.
• 10+ years of applied data science & engineering experience, with deep hands-on expertise in forecasting and predictive modeling.
• Demonstrated experience with model monitoring, debugging, and long-term maintenance in production environments.
• Excellent communication and storytelling skills — able to simplify complexity and influence executive stakeholders.
• Team leadership experience with a track record of building high-performing, engaged teams.
• An advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research).
• A self-directed, intellectually curious mindset and comfort leading ambiguous projects from 0→1.
• The ability to thrive in a dynamic environment — flexible, resourceful, and willing to do what it

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