Machine Learning Data Scientist - Consumer Forecasting

Details

The Strategic Finance Data Science team is building forecasting capabilities to support data-driven decision-making across user growth, revenue, compute infrastructure, pricing, operational efficiency, and growth strategy.

This role will lead forecasting initiatives as a founding member of the Forecasting pillar. The position involves building and scaling robust, interpretable, and production-ready forecasting systems for metrics including DAU/WAU, revenue, customer lifetime value, compute consumption, and profitability. The role is highly cross-functional and requires collaboration with product managers, researchers, engineers, and finance leaders.

The role is based in San Francisco, California, and follows a hybrid work model with three days per week in the office.

Responsibilities

• Build statistical and machine learning models for forecasting needs across product, finance, infrastructure, and go-to-market domains.
• Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development and prototyping, experimentation, deployment, monitoring, and explainability.
• Develop and productionize scalable, interpretable forecasts for user growth, monetization, compute load, customer lifetime value, and profitability.
• Contribute to self-service forecasting tools and internal platforms that enable teams across OpenAI to access and act on real-time predictions.
• Research and evaluate emerging forecasting tools and techniques, including TimeGPT, large language model extensions, causal forecasting, and hybrid approaches.
• Translate technical outputs into business-aligned recommendations and decision frameworks.
• Collaborate with cross-functional teams to integrate forecasts into planning processes, experimentation workflows, and executive decision-making.

Requirements

• Advanced degree, such as an MS or PhD, in a quantitative field such as Statistics, Computer Science, Economics, or Operations Research.
• At least 7 years of experience in applied data science, with deep hands-on experience in forecasting, predictive modeling, or marketplace systems.
• Expertise in time-series forecasting techniques and practical understanding of trade-offs between performance, explainability, and scalability.
• Proficiency in Python and SQL.
• Experience with scikit-learn, PyTorch or TensorFlow, and forecasting libraries.
• Demonstrated experience with model monitoring, debugging, and long-term maintenance in production environments.
• Strong communication and storytelling skills, including the ability to simplify complex topics and influence executive stakeholders.
• Ability to lead ambiguous projects from 0 to 1, with a self-directed and intellectually curious approach.

Bonus Qualifications

• Experience building or scaling forecasting platforms in a high-growth company.
• Familiarity with causal inference and Bayesian forecasting.
• Experience building agentic decision-support systems.
• Passion for AI and a strong perspective on how machine learning should inform strategic decisions in fast-moving environments.

Benefits

• Base salary of $347,000–$445,000 per year.
• Equity and performance-related bonuses for eligible employees.
• Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
• Pre-tax accounts for health, dependent care, and commuter expenses.
• 401(k) retirement plan with employer match.
• Paid parental, medical, and caregiver leave.
• Paid time off, paid company holidays, office closures, and sick or safe time as required by law.
• Mental health and wellness support.
• Employer-paid basic life and disability coverage.
• Annual learning and development stipend.
• Daily office meals and eligible meal delivery credits.
• Relocation support for eligible employees.
• OpenAI is an equal opportunity employer and provides reasonable accommodations to applicants with disabilities.