The opportunity
OpenAI’s Agentic Data Science team helps shape how AI agents are built, deployed, and improved across our products. We partner with product, engineering, research, and security teams to define meaningful measures of success, understand how our systems behave in the real world,…
What you'll do
Define how we measure AI-agent security. Establish metrics and evaluation: frameworks for security-control coverage, agent behavior, sensitive actions, access patterns, detection quality, and emerging risks.
Improve security controls without introducing unnecessary friction. Quantify: the effectiveness and operational costs of safeguards, including false positives, blocked actions, escalations, approval delays, and recovery paths. Help teams make controls safer, more precise, and easier to use.
Build the data foundations for security decisions. Partner with engineering: and data teams to improve instrumentation, connect fragmented telemetry, establish trusted datasets, and surface important coverage and data-quality gaps.
Strengthen detection and response. Identify meaningful signals of anomalous: behavior, risky access, sensitive-data exposure, and other security-relevant activity. Evaluate whether interventions improve detection quality, response times, and real-world security outcomes.
Shape AI-powered cybersecurity products. Partner with product, engineering,: and research teams to assess how effectively AI systems identify security issues, support developer and enterprise workflows, and create measurable customer value.
Develop evaluation systems for security findings. Define quality measures for: findings, including accuracy, severity, actionability, duplication, resolution, and downstream impact. Connect model behavior and product changes to outcomes such as triage, remediation, and vulnerability reduction.
What they're looking for
- Understand the complete security workflow. Measure how users discover,: investigate, validate, prioritize, and resolve security issues. Identify opportunities to improve activation, adoption, retention, and enterprise value across customer-facing cybersecurity products.
- Design rigorous measurement and experimentation strategies. Evaluate new: models, security controls, product features, and workflows through controlled experiments, staged rollouts, observational analyses, and other methods appropriate for high-stakes environments.
- Translate analysis into security and product strategy. Identify the: highest-value decisions, clarify tradeoffs, recommend where teams should invest, and communicate findings clearly to technical partners and senior leadership.
- Help establish a new security data science capability. Build a focused: roadmap, create durable operating rhythms across Data Science and Security, and help shape how this discipline grows over time.