Applied Data Science & Machine Learning Leader - Global Forecasting

OpenAI•San Francisco, CA

6d•Hybrid

About The Position

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 a scalable forecasting platform 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 hands-on technical leader to build and lead a small but mighty team of applied data scientists and ML engineers to develop forecasting capabilities and platforms from the ground up. Your team will be responsible for building and scaling robust, interpretable, and production-ready forecasting systems. These models will power critical business decisions by predicting core metrics such as DAU/WAU, revenue, compute consumption, and profitability. You will work closely with Strategic Finance leaders to integrate these scalable forecasting capabilities into their operational rhythms and collaborate with the Growth team to forecast key KPIs. This is a highly cross-functional role that requires technical excellence, strong product intuition, and business acumen. You’ll collaborate with various partners to operationalize forecasting insights, influence company-wide strategy, and build foundational forecasting capabilities at OpenAI. This role is based in San Francisco, CA. We follow a hybrid work model of three days in the office per week and offer relocation assistance to new employees.

Requirements

Extensive experience building production-ready models for time series applications.
Proven track record of building adjustable and explainable forecast models for multiple planning cycles.
10+ years of applied data science & engineering experience, with deep hands-on expertise in forecasting and predictive modeling.
Demonstrated experience with model monitoring, debugging, and long-term maintenance in production environments.
Excellent communication and storytelling skills — able to simplify complexity and influence executive stakeholders.
Team leadership experience with a track record of building high-performing, engaged teams.
An advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research).
A self-directed, intellectually curious mindset and comfort leading ambiguous projects from 0→1.
The ability to thrive in a dynamic environment — flexible, resourceful, and willing to do what it takes to succeed.
Experience building or scaling forecasting platforms in a high-growth company.
A passion for AI and a strong perspective on how machine learning should inform strategic decisions in fast-moving environments.
Experience building or scaling forecasting platforms in a high-growth company.
A passion for AI and a strong perspective on how machine learning should inform strategic decisions in fast-moving environments.

Nice To Haves

Experience building or scaling forecasting platforms in a high-growth company.
A passion for AI and a strong perspective on how machine learning should inform strategic decisions in fast-moving environments.

Responsibilities

Build and manage a world-class team of applied data scientists and ML engineers to develop forecasting platforms at scale.
Design and own the roadmap for the forecasting platform in partnership with cross-functional stakeholders.
Collaborate closely with Strategic Finance teams to ensure forecasts are well integrated into planning processes, and executive decision-making.
Work closely with cross-functional partners to help them adopt scientific, automated forecasting solutions.
Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development and prototyping, experimentation, deployment, monitoring, and explainability.
Research and evaluate emerging tools and techniques in the forecasting space.
Translate technical outputs into business-aligned recommendations and decision frameworks.

Stand Out From the Crowd

Upload your resume and get instant feedback on how well it matches this job.

Upload and Match Resume