Machine Learning Data Scientist - Consumer Forecasting

OpenAI•San Francisco, CA

1d•$347,000 - $445,000•Hybrid

About The Position

The Machine Learning Data Scientist will be a founding member of the Forecasting pillar within OpenAI’s Strategic Finance Data Science function. The role leads initiatives to build and scale robust, interpretable, production-ready forecasting systems that support business decisions across user growth, revenue, compute infrastructure, and other areas. Forecasts will inform planning, pricing, operational efficiency, growth strategy, and investment decisions. The position combines technical modeling expertise with product intuition and business acumen. It partners with product managers, researchers, engineers, and finance leaders to operationalize forecasting insights, influence company-wide strategy, and establish foundational forecasting capabilities. The role is based in San Francisco, California, with a hybrid work model requiring three days in the office per week. Relocation assistance is offered 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 hands-on exposure to forecasting, predictive modeling, or marketplace systems.
Expertise in time-series forecasting techniques and practical understanding of trade-offs among model performance, explainability, and scalability.
Proficiency in Python, SQL, and tools such as 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.
Self-directed, intellectually curious, and comfortable leading ambiguous projects from 0 to 1.

Nice To Haves

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.

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 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.