AI Regulatory Risk Quantification Engineer

Felvix Tech

Remote · $130k – $160k / year · Full-time · Posted 4 months ago

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About the role

Regulatory risk is qualitative until someone has to put a number on it. When a CISO presents to the board, they need to say 'our current compliance gaps expose us to an estimated $12-18M in regulatory penalties over the next 24 months' — not 'we have some compliance issues.' We help 60 enterprise clients move from qualitative to quantitative regulatory risk assessments, and the model that produces those estimates is what this role owns. You'll build the risk quantification engine: combining regulatory penalty databases, enforcement action histories, firm-specific gap profiles, and industry exposure models to produce calibrated monetary risk estimates with confidence intervals. The regulatory landscape changes constantly — new guidance, enforcement trend shifts, precedent-setting penalties — and the model needs to stay current automatically. This is a senior IC role requiring both ML depth and regulatory domain knowledge. You'll work with our policy analysts who track regulatory developments and our client risk officers who use your model outputs to brief boards. The standard is calibration: if you say 80% confidence interval, the true penalty should fall inside 80% of the time.

Fully remote.

Responsibilities

Build calibrated monetary risk quantification models for regulatory compliance gaps
Integrate regulatory penalty databases and enforcement action histories into risk estimates
Develop firm-specific risk profiles combining gap severity and enforcement exposure
Implement automatic model updates as new enforcement precedents emerge
Create confidence interval reporting meeting board-presentation standards
Validate model calibration against realized regulatory penalties in historical cases

Requirements

6+ years in quantitative risk modeling, actuarial analytics, or regulatory compliance analytics
Strong statistical modeling and probability calibration skills
Experience with regulatory data sources: SEC EDGAR, FINRA enforcement, EU supervisory actions
Python proficiency for model development and data pipeline work
Ability to communicate probabilistic outputs to executive and board-level audiences