Machine Learning Data Scientist - Consumer Forecasting

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 scalable forecasting infrastructure 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 senior Machine Learning Data Scientist to lead our forecasting initiatives. You’ll be one of the founding members of the Forecasting pillar within Strategic Finance Data Science, responsible for building and scaling robust, interpretable, and production-ready forecasting systems. Your models will power critical business decisions by predicting core metrics such as 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’ll collaborate with product managers, researchers, engineers, and finance leaders to operationalize forecasting insights, influence company-wide strategy, and build foundational forecasting capabilities at OpenAI.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will

• Build statistical and machine learning models to solve forecasting needs across product, finance, infrastructure, and GTM 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, enabling teams across OpenAI to access and act on real-time predictions.

• Research and evaluate emerging tools and techniques in the forecasting space, 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 ensure forecasts are well-integrated into planning processes, experimentation workflows, and executive decision-making.

You might thrive in this role if you have

• Advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, 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 if you have

• Experience building or scaling forecasting platforms in a high-growth company.

• Familiarity with causal inference, 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.