Quantitative Intelligence Analyst

OpenAI OpenAI · AI Frontier · San Francisco, CA · Intelligence & Investigations

This role focuses on discovering and defining novel quantitative risk signals in complex human-AI systems before they are well-defined, measurable, or widely understood. The analyst will use subject matter expertise and quantitative tooling to surface early risk signals, build models to explain harm emergence, and translate patterns into data-driven insights. The work informs policy gaps, operationalizes unmeasured problems, and supports detection and mitigation efforts. The role involves developing analytical frameworks for risk evolution, designing adversarial scenarios, and producing briefs for strategic risk prioritization.

What you'd actually do

  1. Discover and define new quantitative risk signals where no established metrics exist, using subject matter expertise to surface early, weak, or unconventional indicators
  2. Translate complex trust and safety challenges into measurable signals that can be tracked and stress-tested over time
  3. Develop upstream early-warning and signal frameworks that inform downstream detection and mitigation efforts
  4. Analyze risk trends to assess the underlying drivers and causal factors behind those changes
  5. Conduct data mining and statistical modeling to understand how risks originate, evolve, and propagate across systems
  6. Design adversarial scenarios, and quantitative stress tests to assess exposure, coverage gaps, and vulnerabilities
  7. Produce clear data-driven briefs to support risk prioritization, contingency planning, and strategic risk products across teams

Skills

Required

  • 3+ years of experience in quantitative intelligence analysis, trust & safety, security analysis, or risk-focused research
  • Comfortable working on complex trust and safety domains such as child safety, violent activities, self-harm, or similar high-stakes risk areas
  • Familiarity with data mining, statistical modeling, and supervised learning methods
  • Understand how to monitor signals or models for data drift, behavioral adaptation, or performance degradation over time, and can diagnose likely causes
  • Experience in operationalizing adversarial or strategic risk behaviors, including through red-team exercises, agent-based modeling, or structured scenario analyses
  • Comfortable working with Python and SQL

Nice to have

  • Experience with quantitative stress testing or Monte Carlo simulations to assess uncertainty and tail risk

What the JD emphasized

  • novel and emerging risks
  • early, weak, and unconventional risk signals
  • ambiguous patterns
  • previously unmeasured problems
  • new risks form, evolve, and propagate
  • adversarial scenarios
  • quantitative stress tests
  • complex trust and safety domains
  • data drift, behavioral adaptation, or performance degradation
  • adversarial or strategic risk behaviors
  • red-team exercises
  • agent-based modeling
  • structured scenario analyses

Other signals

  • Develops analytic models that explain how harms could emerge
  • Translates ambiguous patterns into structured, data-driven insight
  • Develops analytical frameworks that map how new risks form, evolve, and propagate
  • Designs adversarial scenarios, and quantitative stress tests