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OpenAIOpenAI

Data Scientist, Safety

Data Scientist focused on safety at OpenAI, building analytics to measure harmful behavior, detect fraud, evaluate safety systems, and inform critical decisions on AI deployment. Requires strong SQL/Python, statistical reasoning, and experience with causal analysis.

About the job

Responsibilities

  • Measure harmful or abusive behavior across OpenAI’s products
  • Detect fraud, manipulation, and coordinated misuse
  • Evaluate and improve safety classifiers, rules systems, mitigation systems, and human review workflows
  • Design experiments and causal analyses to understand product, policy, and mitigation impacts
  • Build prevalence estimators, dashboards, monitoring systems, and executive decision frameworks
  • Diagnose gaps in safety and integrity systems using behavioral and product data
  • Quantify and navigate false positive / false negative tradeoffs
  • Translate ambiguous safety risks into measurable problems and evidence-based recommendations
  • Partner with Product, Engineering, Policy, Research, and Operations teams to improve safety outcomes
  • Build zero-to-one analytical systems in rapidly evolving domains

Requirements

  • Strong statistical reasoning and analytical judgment
  • Experience with experimentation, causal inference, or observational analysis
  • Strong SQL and Python skills
  • Experience working with messy, incomplete, or noisy datasets
  • Ability to structure open-ended business or risk problems
  • Excellent communication with technical and non-technical stakeholders
  • High ownership and comfort operating independently

Nice-to-Haves

  • Background in Trust & Safety / Integrity
  • Experience with Fraud & abuse
  • Security analytics experience
  • AI/ML model measurement and evaluation
  • Alignment and AI safety research
  • Background in Biosecurity, synthetic biology, infectious diseases, or computational biology

Compensation & Benefits

  • Compensation Range: $230K - $325K USD

Skills

Python, SQL, Statistical Analysis, Causal Inference, Experimentation, Data Analysis, A/B Testing, Observational Studies

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