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OpenAI's Strategic Finance team is building a world-class forecasting capability to drive data-driven decision-making across the company. As a senior Machine Learning Data Scientist, you'll lead the founding of the Forecasting pillar within Strategic Finance Data Science, responsible for building and scaling robust, interpretable, production-ready forecasting systems that power critical business decisions.
You'll own the end-to-end modeling lifecycle for forecasting core metrics including DAU/WAU, revenue, LTV, compute consumption, and profitability. This spans scoping, feature engineering, model development, experimentation, deployment, monitoring, and explainability. Your work will directly inform planning, pricing, operational efficiency, and growth strategy across the organization.
Key responsibilities include: developing statistical and machine learning models for forecasting across product, finance, infrastructure, and go-to-market domains; building and productionizing scalable, interpretable forecasts; contributing to self-service forecasting tools and internal platforms; researching emerging forecasting techniques (TimeGPT, LLM extensions, causal forecasting); translating technical outputs into business-aligned recommendations; and collaborating with product, engineering, finance, and executive teams to integrate forecasts into planning and decision-making workflows.
You should have an advanced degree (MS/PhD) in a quantitative field, 7+ years of applied data science experience with deep expertise in forecasting and predictive modeling, strong proficiency in Python and SQL, hands-on experience with scikit-learn, PyTorch/TensorFlow, and forecasting libraries, and demonstrated success with model monitoring and production maintenance. Bonus experience includes building forecasting platforms at high-growth companies, causal inference, and Bayesian forecasting.
This is a highly cross-functional role requiring technical excellence, strong product intuition, business acumen, and the ability to lead ambiguous 0→1 projects. You'll work hybrid (3 days/week in office) in San Francisco with relocation assistance available.
