# Data Scientist, Cybersecurity

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** Remote
**Role:** Data Science
**Salary:** $263k – $515k/yr
**Experience:** 5+ years
**Skills:** SQL, Python, Experimentation, Causal Inference, Observational Analysis, Metric Design, Evaluation Frameworks, Anomaly Detection, Detection Engineering, Threat Research, Identity And Access Management, LLMs, Model Evaluation, Automated Monitoring, Data Instrumentation
**Posted:** 2026-08-17

> This senior data scientist will define measurement frameworks for AI-agent security, evaluate controls and security findings, and improve detection and response outcomes. The role requires 5+ years of quantitative experience, strong SQL and Python skills, and experience with cybersecurity or other adversarial-risk domains.

## Job Description

## Responsibilities
- Define metrics and evaluation frameworks for AI-agent security, including security-control coverage, agent behavior, sensitive actions, access patterns, detection quality, and emerging risks.
- Quantify the effectiveness and operational costs of security safeguards, including false positives, blocked actions, escalations, approval delays, and recovery paths.
- Partner with engineering and data teams to improve instrumentation, connect fragmented telemetry, establish trusted datasets, and identify data-quality gaps.
- Identify 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.
- Assess AI-powered cybersecurity products and their value in developer and enterprise workflows.
- Define quality measures for security findings, including accuracy, severity, actionability, duplication, resolution, and downstream impact.
- Measure how users discover, investigate, validate, prioritize, and resolve security issues, and identify opportunities to improve activation, adoption, retention, and enterprise value.
- Design experiments, staged rollouts, observational analyses, and other measurement strategies for models, security controls, product features, and workflows.
- Translate analysis into security and product strategy and communicate recommendations to technical partners and senior leadership.
- Build a roadmap and operating rhythms for a new security data science capability.

## Requirements
- 5+ years of experience in data science, applied research, analytics, or a related quantitative field.
- Experience in cybersecurity, trust and safety, fraud or abuse prevention, privacy, platform integrity, or a related adversarial-risk domain.
- Strong proficiency in SQL and Python.
- Experience investigating complex datasets, working with incomplete instrumentation, and building reproducible analytical workflows.
- Experience defining metrics and evaluation frameworks when ground truth is limited, outcomes are delayed, or risks are difficult to observe directly.
- Strong judgment in experimentation, causal inference, observational analysis, and measurement limitations.
- Ability to collaborate with security engineers, product managers, software engineers, researchers, data engineers, and senior leaders.
- Ability to translate technical analysis into improvements to products, systems, controls, or organizational priorities.
- Ability to operate independently, define a roadmap, and structure an emerging domain.

## 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 with user experience, including false positives, approval flows, operational burden, and recovery behavior.
- Experience building automated monitoring, anomaly detection, production-oriented data assets, or systems connecting model outputs to real-world outcomes.
- Experience establishing cross-functional measurement programs or analytical capabilities from the ground up.

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