Machine Learning Data Scientist, Forecasting

Details

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

This role will be a founding member of the Forecasting pillar, responsible for building and scaling robust, interpretable, and production-ready forecasting systems. The models will predict metrics such as DAU/WAU, revenue, customer lifetime value, compute consumption, and profitability. The position requires technical excellence, product intuition, business acumen, and close 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 in the office per week.

Responsibilities

• Build statistical and machine learning models for forecasting across product, finance, infrastructure, and go-to-market domains.
• Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development, 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 to access and act on real-time predictions.
• Research and evaluate 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 including 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 among performance, explainability, and scalability.
• Proficiency in Python, SQL, scikit-learn, PyTorch or TensorFlow, and forecasting libraries.
• 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 in a self-directed and intellectually curious manner.

Preferred Qualifications

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

Benefits

• Base salary of $230,000–$385,000 per year.
• Equity, performance-related bonuses for eligible employees, and benefits.
• Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
• Pre-tax Flexible Spending Accounts and commuter benefits.
• 401(k) retirement plan with employer match.
• Paid parental, medical, and caregiver leave.
• Paid time off, paid company holidays, office closures, and paid sick or safe time.
• 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.
• Additional benefits may include charitable donation matching and wellness stipends.

OpenAI is an equal opportunity employer and is committed to providing reasonable accommodations to applicants with disabilities. Background checks are administered in accordance with applicable law.