OpenAI Data Scientist at OpenAI builds and tests ML solutions for personalization, recommendations, and customer lifecycle management. The role includes end-to-end ownership from data preparation to launch evaluation.
Responsibilities
We’re looking for a data scientist who can enter an unfamiliar product area, find a useful ML application, and turn it into a working product intervention. You will own the work from problem definition and data preparation through modeling, experimentation, and launch evaluation.
Projects will include personalization, next-best-action recommendations, lifecycle management, promotion targeting, and models of retention and customer lifetime value. You will help identify opportunities, build proofs of concept, evaluate, and iterate.
Identify opportunities with product and business partners, define the decision a model will inform, and prioritize work by user value, feasibility, and expected impact.
Build datasets, features, reproducible training workflows, and models. You’ll use a combination of tools appropriate to the task at hand.
Translate predictions into product actions. Design experiments and use causal reasoning to establish whether those actions improve user and business outcomes.
Partner with engineers on integration, serving or batch scoring, monitoring, and rollout. Stay involved through launch and iteration.
Explain results and limitations clearly, document decisions, and build reusable methods that other teams can adopt.
You might thrive in this role if you:
Have built and evaluated applied ML systems that influenced a product or business decision, with clear ownership of the modeling work.
Write effective Python and SQL and can investigate imperfect data, debug models, and make pragmatic implementation choices.
Build and schedule production scoring pipelines using dbt, airflow, and flyte.
Understand supervised learning, evaluation, experimentation, and the difference between predicting an outcome and changing it.
Qualification
Proficient in Python and SQLUnderstanding of supervised learningExperience with recommendation systems
Required
Experience building and evaluating applied ML systems influencing product or business decisions
Proficient in Python and SQL
Experience building and scheduling production scoring pipelines
Understanding of supervised learning, evaluation, and experimentation
Preferred
Experience with recommendation systems, personalization, growth, retention, lifetime value, or treatment-effect modeling
