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OpenAI's Agentic Data Science team is seeking a senior data scientist to define effective cybersecurity measurement and strategy in the age of AI agents. This is a high-ownership role establishing a new analytical discipline at the intersection of AI capabilities and security risks.
You will work across OpenAI's Security organization and cybersecurity product teams to measure emerging risks, improve internal security controls, and shape AI-powered security products. Key responsibilities include:
- Define metrics and evaluation frameworks for security-control coverage, agent behavior, sensitive actions, access patterns, and detection quality in AI systems.
- Quantify the effectiveness and operational costs of safeguards, including false positives, blocked actions, and approval delays, to improve controls without introducing unnecessary friction.
- Build data foundations for security decisions by improving instrumentation, connecting fragmented telemetry, and establishing trusted datasets.
- Identify meaningful signals of anomalous behavior, risky access, and sensitive-data exposure; evaluate whether interventions improve detection quality and real-world security outcomes.
- Assess how effectively AI systems identify security issues and support developer and enterprise workflows.
- Define quality measures for security findings (accuracy, severity, actionability, resolution) and connect model behavior to outcomes like triage and vulnerability reduction.
- Measure complete security workflows—how users discover, investigate, validate, prioritize, and resolve issues—to improve adoption and enterprise value.
- Design rigorous measurement and experimentation strategies appropriate for high-stakes environments, including controlled experiments and staged rollouts.
- Translate analysis into security and product strategy, identifying high-value decisions and communicating findings to technical partners and senior leadership.
- Help establish a new security data science capability, build roadmaps, and create operating rhythms across Data Science and Security teams.
You bring 5+ years of experience in data science, applied research, or analytics with a track record of owning ambiguous, high-impact problems. Ideally, you have domain expertise in cybersecurity, trust and safety, fraud prevention, privacy, or platform integrity. Strong proficiency in SQL and Python is essential, along with experience investigating complex datasets and building reproducible workflows. You excel at defining meaningful metrics when ground truth is limited and outcomes are delayed. You have strong judgment in experimentation, causal inference, and observational analysis. You partner effectively across security engineers, product managers, software engineers, researchers, and senior leaders. Experience with detection engineering, threat research, security operations, or identity and access management is a plus.
