Data Scientist, Personalization & Machine Learning

OpenAI • San Francisco, CA

12d • $347,000 - $445,000

About The Position

The Data Scientist, Personalization & Machine Learning works across product, growth, and monetization to find opportunities for machine learning and bring successful approaches into production. The role owns work from problem definition and data preparation through modeling, experimentation, launch evaluation, and iteration. Projects may include personalization, next-best-action recommendations, lifecycle management, promotion targeting, and models of retention and customer lifetime value. The role partners with product and business stakeholders to identify opportunities, engineers to integrate and operate models, and other teams to share reusable methods.

Requirements

• Experience building and evaluating applied machine-learning systems that influenced a product or business decision, with clear ownership of the modeling work.
• Effective Python and SQL skills, with the ability to investigate imperfect data, debug models, and make pragmatic implementation choices.
• Ability to build and schedule production scoring pipelines using dbt, Airflow, and Flyte.
• Understanding of supervised learning, evaluation, experimentation, and the distinction between predicting an outcome and changing it.
• Ability to scope ambiguous problems, work across disciplines, and explain technical tradeoffs to colleagues with different backgrounds.
• Motivation to help people use AI effectively and learn unfamiliar product domains.
• Interest in taking on challenging, impactful machine-learning problems across multiple areas.

Nice To Haves

• Experience with recommendation systems, personalization, growth, retention, lifetime value, or treatment-effect modeling.

Responsibilities

• Identify opportunities with product and business partners, define the decisions models will inform, and prioritize work by user value, feasibility, and expected impact.
• Build datasets, features, reproducible training workflows, and models using appropriate tools.
• Translate predictions into product actions, design experiments, and use causal reasoning to assess effects on user and business outcomes.
• Partner with engineers on model integration, serving or batch scoring, monitoring, rollout, and post-launch iteration.
• Explain results and limitations clearly, document decisions, and develop reusable methods for other teams.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed