Quantitative Intelligence Analyst

About the Team

The Intelligence and Investigations team seeks to rapidly identify and mitigate abuse and strategic risks to ensure a safe online ecosystem. We are dedicated to identifying emerging abuse trends, analyzing risks, and working with our internal and external partners to implement effective mitigation strategies to protect against misuse. Our efforts contribute to OpenAI's overarching goal of developing AI that benefits humanity.

The Strategic Intelligence & Analysis (SIA) team provides safety intelligence for OpenAI’s products by monitoring, analyzing, and forecasting real-world abuse, geopolitical risks, and strategic threats. Our work informs safety mitigations, product decisions, and partnerships, ensuring OpenAI’s tools are deployed securely and responsibly across critical sectors.

About the Role

As a Quantitative Intelligence Analyst , you will focus on discovering novel and emerging risks in complex human–AI systems before they are well-defined, measurable, or widely understood.

You will use deep subject matter expertise and quantitative tooling to surface weak, early, and unconventional risk signals. You will build analytic models that explain how harms could emerge and translate ambiguous patterns into structured, data-driven insight. Your work will help identify potential gaps in policy or coverage and operationalize previously unmeasured problems into signals that can support detection, mitigation, and planning downstream. You will develop analytical frameworks that map how new risks form, evolve, and propagate as products change, policies shift, and external events unfold. Your analyses will directly inform strategic risk prioritization and planning across the company, with regular visibility through strategic risk products.

This role is based in San Francisco, CA (hybrid, 3 days / week). Relocation support is available

In this role, you will :

Discover and define new quantitative risk signals where no established metrics exist, using subject matter expertise to surface early, weak, or unconventional indicators

Translate complex trust and safety challenges into measurable signals that can be tracked and stress-tested over time

Develop upstream early-warning and signal frameworks that inform downstream detection and mitigation efforts

Analyze risk trends to assess the underlying drivers and causal factors behind those changes

Conduct data mining and statistical modeling to understand how risks originate, evolve, and propagate across systems

Design adversarial scenarios, and quantitative stress tests to assess exposure, coverage gaps, and vulnerabilities

Produce clear data-driven briefs to support risk prioritization, contingency planning, and strategic risk products across teams

You might thrive in this role if you :

Have 3–6+ years of experience in quantitative intelligence analysis, trust & safety, security analysis, or risk-focused research

Are 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