Machine Learning Data Scientist - Consumer Forecasting
$347k - $445k • San Francisco • FullTime
Posted 8h ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
On-site
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
OpenAI is seeking a senior Machine Learning Data Scientist to lead its forecasting initiatives within the Strategic Finance team. This is a founding role for the Forecasting pillar, responsible for building and scaling robust, interpretable, and production-ready forecasting systems. The models developed will be crucial for predicting key business metrics like daily/weekly active users, revenue, lifetime value, compute consumption, and profitability, directly influencing critical business decisions and company strategy. This role requires a blend of technical expertise, product intuition, and business acumen, involving close collaboration with product managers, researchers, engineers, and finance leaders to operationalize insights and build foundational forecasting capabilities.
Responsibilities
- Build statistical and machine learning models for forecasting across product, finance, infrastructure, and GTM domains.
- Manage the end-to-end modeling lifecycle: scoping, feature engineering, 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 to enable broader access to real-time predictions.
- Research and evaluate emerging forecasting tools and techniques, including TimeGPT, LLM extensions, causal forecasting, and hybrid approaches.
- Generate strategic insights by translating technical outputs into business-aligned recommendations and decision frameworks.
- Collaborate with cross-functional teams to integrate forecasts into planning, experimentation, and executive decision-making processes.
Requirements
- 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 understanding of model trade-offs (performance, explainability, scalability).
- Proficiency in Python, SQL, and tools like 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, with the ability to simplify complexity and influence executive stakeholders.
- Self-directed, intellectually curious, and comfortable leading ambiguous projects from inception to completion.
Benefits
- Hybrid work model (3 days in office per week)
- Relocation assistance for new employees