Applied Product Data Scientist – Growth Metrics

OpenAI; California (MO); Hybrid; USD 150,000 - 190,000; Full time

Benefits offered by this job

Relocation assistance

Hybrid work model

Job summary

OpenAI is seeking a Data Scientist for the Applied Product team to help shape data-driven product development across consumer and enterprise products. You will define north-star metrics, design A/B tests, and build source-of-truth dashboards used company-wide.

The role is based in San Francisco, CA or Seattle, WA with a hybrid model (3 days in office) and relocation assistance for new hires. You will embed with cross-functional teams and influence product decisions through rigorous

Qualifications

• 5+ years experience in a quantitative role navigating highly ambiguous environments, ideally as an early data scientist or product analyst at a hyper-growth product company or research org.
• Proposed, designed, and run rigorous experiments with clear insights using SQL and Python
• Defined, implemented, and operationalized new feature and product-level metrics from scratch
• Excellent communication skills with product managers, engineers, and executives

Responsibilities

• Embed with the product development team as a trusted partner
• Define and interpret A/B tests to answer questions about model and UX changes
• Establish data-driven product development culture by tracking metrics
• Develop dashboards, reports, and self-serve data access for the team

Skills

SQL

Python

A/B testing

Data visualization

Metrics & product analytics

NLP/LLMs

Tools

SQL

Python

Job description

OpenAI is seeking a Data Scientist for the Applied Product team to help shape data-driven product development across consumer and enterprise products. You will define north-star metrics, design A/B tests, and build source-of-truth dashboards used company-wide.

The role is based in San Francisco, CA or Seattle, WA with a hybrid model (3 days in office) and relocation assistance for new hires. You will embed with cross-functional teams and influence product decisions through rigorous