Data Scientist, API

OpenAI OpenAI · AI Frontier · San Francisco, CA · Data Science

Data Scientist on the API team responsible for building measurement systems, defining KPIs, identifying developer friction, evaluating launches, and translating data into product decisions to improve reliability and developer outcomes at scale. This role partners with Product, Engineering, Research, and Finance to ensure trusted metrics and rigorous experimentation.

What you'd actually do

  1. Own the core KPI framework for the API platform, spanning developer adoption, engagement, retention, and platform health.
  2. Build end-to-end funnels that identify where developers succeed or get stuck—from first integration through scaling to production.
  3. Define and operationalize platform guardrails (e.g., reliability, latency, error rates, cost/efficiency) and connect them to user outcomes.
  4. Design and evaluate experiments and rollouts to quantify the impact of platform and product changes.
  5. Partner with product and engineering teams to improve instrumentation, data quality, and metric definitions so decisions are fast and correct.

Skills

Required

  • Statistics
  • Causal Inference
  • Experimentation Design
  • SQL
  • Python
  • Data Pipelines
  • Business Intelligence Tools
  • Communication Skills

Nice to have

  • Developer Platforms
  • APIs/SDKs
  • Usage-based Products
  • Platform Reliability Analytics
  • Incident Impact Measurement
  • Performance/Cost Optimization
  • AI Evaluation
  • Quality Measurement Systems
  • Online/Offline Evals
  • Human-in-the-loop
  • Safety/Quality Guardrails

What the JD emphasized

  • 10+ years of experience in data science roles within product or technology organizations
  • Expertise in statistics and causal inference
  • Expert-level SQL and proficiency in Python for analytics, modeling, and experimentation
  • Proven experience designing and interpreting experiments and making statistically sound recommendations
  • Experience building datasets, metrics, and data pipelines that power production decision-making
  • Strong product sense and an impact-driven mindset
  • Ability to operate effectively in a fast-moving, ambiguous environment with limited structure
  • Are consistently among the first to adopt the latest AI tools, you use them daily to increase your own throughput, and you proactively turn them into durable workflows that change how your team and org operate.
  • Familiarity with AI evaluation and quality measurement systems (online/offline evals, human-in-the-loop, safety/quality guardrails).

Other signals

  • developer adoption
  • engagement
  • retention
  • platform health
  • developer friction
  • reliability
  • latency
  • cost/efficiency
  • experimentation
  • product decisions
  • data quality
  • metric definitions
  • AI platform performance
  • developer success
  • platform reliability analytics
  • incident impact measurement
  • performance/cost optimization
  • AI evaluation
  • quality measurement systems
  • online/offline evals
  • human-in-the-loop
  • safety/quality guardrails