Data Scientist, Cybersecurity

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.

About the job

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.

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