Senior Data Scientist

Responsibilities

• Design, train, and ship exploit prediction models against ground-truth exploitation telemetry
• Build robust evaluation metrics including precision, recall, coverage, efficiency, and calibration
• Solve for extreme class imbalance in published CVEs
• Implement dual-model architecture using partial pooling and hierarchical priors
• Engineer features across scanner output, EDR, asset inventory, identity, and cloud posture
• Own monitoring and drift detection
• Publish methodology write-ups, open benchmarks, and conference talks
• Partner with engineering and forward deployed teams to move models into production

Requirements

• Several years of applied machine learning or statistics with production-level models
• Fluency in Python and SQL
• Experience with version control and reproducible pipelines
• Depth in classification under heavy imbalance
• Expertise in at least one of: survival and time-to-event analysis, Bayesian hierarchical modeling, or causal inference
• Ability to quantify uncertainty and explain complex models to security executives

About the Company

Empirical Security leverages ground-truth telemetry to develop predictive, actionable insights that transform how organizations identify, prioritize, and remediate vulnerabilities in cloud, appsec, and traditional environments.