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Applied Data Scientist, Personalization & Machine Learning

OpenAI

Applied Data Scientist, Personalization & Machine Learning is a still-open role at OpenAI on Jobrow. Location: Hybrid · San Francisco · California · United States. Listed pay: $347k–$445k. Sourced from the company's public Ashby board. First seen 1 hour ago; last confirmed 1 hour ago. Apply on the employer ATS — Jobrow is not the employer.

Pay
$347k–$445k
Workplace
Hybrid
Location
San Francisco · California · United States
First seen
1 hour ago
Last seen
1 hour ago
Board
Ashby
Apply

About this role

About the Team We work across product, growth, and monetization to help people get more value from OpenAI’s products.

  • Function: Machine learning
  • Seniority: mid
  • python
  • sql

Location & pay

Hybrid · San Francisco · California · United States Listed pay on Jobrow: $347k–$445k.

Source

Jobrow reads the public Ashby board for OpenAI. First seen 1 hour ago; last seen 1 hour ago. We are not the employer — apply only on the company ATS.

Posting

About the Team We work across product, growth, and monetization to help people get more value from OpenAI’s products. We identify opportunities for machine learning, build and test early solutions, and partner with engineering and product to bring successful approaches into production. About the Role 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. In this role, you will: - 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. - Can scope ambiguous problems, work across disciplines, and explain technical tradeoffs to colleagues with different backgrounds. - Are motivated by helping people use AI effectively and by learning unfamiliar product domains. - Are excited to take on challenging, impactful ML problems across multiple areas. - Have experience with recommendation systems, personali

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