Machine Learning Data Scientist, Forecasting
OpenAI · Data Science
- Location
- San Francisco, California, United States · Remote
- Metro
- San Francisco Bay Area
- Workplace
- Hybrid
- Level
- Senior
- Type
- Full-time
- Pay
- $340K–$500K/yr (published by employer)
- Posted
- 2 months ago
- Source
- Ashby
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What the posting covers
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.…
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,…
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,…
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.…
Bonus if you have
Experience building or scaling forecasting platforms in a high-growth company. Familiarity with causal inference, Bayesian forecasting Passion for AI and a strong point of view on how machine learning should inform strategic decisions in fast-moving…
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our…
Excerpts only. The employer’s page has the complete posting.