Data Scientist, Cybersecurity

OpenAI

1d•$263,000 - $515,000

About The Position

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, and translate evidence into better decisions. As AI agents become more capable, they can write and execute code, access sensitive systems, and complete increasingly complex tasks with greater autonomy. These capabilities create powerful opportunities to improve cybersecurity, but they also introduce risks that traditional security tools and processes were not designed to address. Meeting this moment requires new ways to measure security, evaluate defenses, and distinguish genuine risk reduction from friction that slows users down. We are looking for a senior data scientist to help define what effective cybersecurity looks like in the age of AI agents. 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. The problems are foundational: How do we know whether an agent’s security controls are effective? Which safeguards meaningfully reduce risk, and which create unnecessary friction? When an AI system identifies a potential vulnerability, how do we determine whether the finding is accurate, actionable, and ultimately resolved? How do we detect anomalous behavior or risky access when the systems themselves are changing rapidly? You will report into Data Science while partnering closely with Security, Cyber Product, Engineering, and Research. This is a high-ownership role for someone who can establish a new analytical discipline, operate across organizational boundaries, and turn ambiguous security challenges into measurable improvements.

Requirements

5+ years of experience in data science, applied research, analytics, or a related quantitative field, with a track record of owning ambiguous, high-impact problems.
Experience in cybersecurity, trust and safety, fraud or abuse prevention, privacy, platform integrity, or another domain involving adversarial behavior and difficult-to-measure risks.
Strong proficiency in SQL and Python, including experience investigating complex datasets, working through incomplete instrumentation, and building reproducible analytical workflows.
Experience defining meaningful metrics and evaluation frameworks when ground truth is limited, outcomes are delayed, or important risks cannot be observed directly.
Strong judgment in experimentation, causal inference, observational analysis, and the practical limitations of different measurement approaches.
The ability to partner effectively with security engineers, product managers, software engineers, researchers, data engineers, and senior leaders.
A demonstrated ability to translate technical analysis into concrete improvements in products, systems, controls, or organizational priorities.
Comfort operating independently, defining a roadmap, and bringing structure to a domain without established processes or industry standards.

Nice To Haves

Experience with detection engineering, threat research, security operations, insider risk, identity and access management, or privacy-preserving security analytics.
Familiarity with AI agents, large language models, model evaluations, automated code review, or AI-powered cybersecurity products.
Experience evaluating security findings, vulnerability detection, remediation workflows, or developer-facing security tools.
Experience balancing security effectiveness against user experience, including false positives, approval flows, operational burden, and recovery behavior.
Experience building automated monitoring, anomaly detection, production-oriented data assets, or systems that connect model outputs to real-world outcomes.
A track record of building new cross-functional measurement programs or establishing analytical capabilities from the ground up.

Responsibilities

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.
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.