Applied Data Scientist, Personalization & Machine Learning

Highlights

• Build personalization and next-best-action recommendations for OpenAI products.
• Model retention and lifetime value to drive growth and monetization.
• Own ML from problem definition through launch evaluation and iteration.

Ideal candidate

Applied ML builder who owns ambiguous product problems end-to-end and turns predictions into shipped actions. Pragmatic, cross-functional collaborator motivated by helping people get more value from AI.

Requirements

• Built and evaluated applied ML systems influencing product or business decisions with clear modeling ownership
• Strong Python and SQL skills with ability to investigate imperfect data and debug models
• Experience building and scheduling production scoring pipelines using dbt, Airflow, and Flyte
• Solid understanding of supervised learning, evaluation, experimentation, and causal reasoning
• Ability to scope ambiguous problems and explain technical tradeoffs cross-functionally
• Experience with recommendations, personalization, retention, LTV, or treatment-effect modeling is useful