Note: The job is a remote job and is open to candidates in USA. Empirical Security develops cybersecurity vulnerability models using ground-truth telemetry to help organizations identify, prioritize, and remediate vulnerabilities. The Senior Data Scientist will own customer-specific models and data end to end, from problem framing and feature engineering through evaluation, deployment, monitoring, and communicating results to customers.
Responsibilities
- Design, train, and ship exploit prediction models against ground-truth exploitation telemetry, in cloud, appsec, and traditional infrastructure
- Build evaluation that survives contact with reality. Precision, recall, coverage, efficiency, calibration, and how all four decay over time. Accuracy is not a number you report once at launch
- Solve for extreme class imbalance. A fraction of a percent of published CVEs are ever exploited in the wild, and most of the industry's modeling failures start with pretending that isn't true
- Work the hard part of the dual-model architecture: partial pooling, hierarchical priors, and cold-start behavior for customers whose local telemetry is thin in month one and rich in month twelve
- Engineer features across scanner output, EDR, asset inventory, identity, cloud posture, and exploitation telemetry, and be honest about which ones are leakage
- Own monitoring and drift detection
- Publish. Papers, methodology write-ups, open benchmarks, conference talks. Our positioning is that we show our work
- Partner with engineering and our forward deployed team to move models out of notebooks and into production systems that customers depend on
Skills
- Several years of applied machine learning or statistics with models that ran in production and had consequences when they were wrong
- Fluency in Python and SQL, and the discipline that comes with version control, reproducible pipelines, and secure handling of customer data
- Real depth in classification under heavy imbalance, plus at least one of: survival and time-to-event analysis, Bayesian hierarchical modeling, or causal inference
- Calibration instincts. You should be visibly uncomfortable when a model outputs 0.9 and is right 60% of the time
- The ability to explain a model to a security executive, and to quantify uncertainty out loud rather than burying it in an appendix
- Enough curiosity about attacker behavior to ask why a feature works, not just whether it does
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