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# Data Scientist, Codex

- **Company:** OpenAI Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $230,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Software Quality, Programming Tools, Python (Programming Language), SQL Databases, Large Language Models
- **Published:** August 25, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pecdvge2x7

## About the Role

* 5+ years in a quantitative role at a developer-facing or high-growth product
 * Fluency in SQL and Python; comfort with experiment design and causal inference
 * Experience defining product metrics tied to user value
 * Ability to communicate clearly with PM, Eng, and Design-and to influence product direction

You could be an especially great fit if you have

 * Strong programming background; ability to prototype, run simulations, and reason about code quality
 * Familiarity with IDE/extension telemetry or developer tooling analytics
 * Prior experience with NLP/LLMs, code models, or evaluations for generative coding

## Description

As a Data Scientist on Codex, you will measure and accelerate product-market fit for AI developer tools. You'll define what "developer productivity" means for our product, run experiments on new coding models and UX, and pinpoint where the model helps or hurts across languages and tasks. Your insights will directly shape how an entire industry builds software.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will

 * Embed with the Codex product team to discover opportunities that improve developer outcomes and growth
 * Design and interpret A/B tests and staged rollouts of new coding models and product features
 * Define and operationalize metrics such as suggestion acceptance, edit distance, compile/test pass rates, task completion, latency, and session productivity
 * Build dashboards and analyses that help the team self-serve answers to product questions (by language, framework, repo size, task type)
 * Diagnose failure modes and partner with Research on targeted improvements (model quality signals, user feedback, evals)


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