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OpenAI's Strategic Finance team is building a best-in-class forecasting capability to drive data-driven decision-making across user growth, revenue, compute infrastructure, and company strategy. You will be a founding member of the Forecasting pillar within Strategic Finance Data Science, responsible for building and scaling robust, interpretable, and production-ready forecasting systems that power critical business decisions.
Your models will predict core metrics including DAU/WAU, revenue, LTV, compute consumption, and profitability. This is a highly cross-functional role requiring technical excellence, strong product intuition, and business acumen. You will collaborate with product managers, researchers, engineers, and finance leaders to operationalize forecasting insights and influence company-wide strategy.
Key responsibilities:
- Build statistical and machine learning models to solve forecasting needs across product, finance, infrastructure, and go-to-market domains
- Own the end-to-end modeling lifecycle: 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 enabling teams across OpenAI to access real-time predictions
- Research and evaluate emerging forecasting techniques such as TimeGPT, large language model extensions, causal forecasting, and hybrid approaches
- Drive strategic insight generation by translating technical outputs into business-aligned recommendations and decision frameworks
- Collaborate closely with cross-functional teams to integrate forecasts into planning processes, experimentation workflows, and executive decision-making
The role is based in San Francisco with a hybrid work model of 3 days in the office per week. OpenAI offers relocation assistance to new employees.
Requirements:
- Advanced degree (MS or PhD) in a quantitative field such as Statistics, Computer Science, Economics, or Operations Research
- 7+ years of experience in applied data science with deep hands-on exposure to forecasting, predictive modeling, or marketplace systems
- Expertise in time-series forecasting techniques and practical understanding of model trade-offs across performance, explainability, and scalability
- Proficiency in Python, SQL, and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries
- Demonstrated experience with model monitoring, debugging, and long-term maintenance in production environments
- Strong communication and storytelling skills—able to simplify complexity and influence executive stakeholders
- Self-directed, intellectually curious, and comfortable leading ambiguous projects from 0→1
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 point of view on how machine learning should inform strategic decisions in fast-moving environments
