How to Become a Model Risk Manager: A Complete Roadmap

A Model Risk Manager makes sure the quantitative and AI/ML models an organization relies on are sound, validated, and well-governed — a discipline born in finance and now expanding fast into machine learning.

What the role owns

• The model risk management framework and model inventory
• Independent model validation and effective challenge
• Ongoing monitoring for performance, drift, and bias
• Governance under SR 11-7 and the NIST AI RMF

Skills

Strong quantitative grounding (statistics, ML basics), the judgment to challenge model assumptions, and the governance fluency to document and report model risk.

Certifications

FRM or PRM for quantitative risk, CRISC for GRC, and AI-specific credentials (IAPP AIGP) as models go ML-heavy. Full credential details and salary data are in the GRC Certifications Guide.

The path

1. Build quant fundamentals — statistics and model concepts.
2. Learn validation — SR 11-7 and the model lifecycle.
3. Certify — FRM/PRM + CRISC.
4. Extend to AI/ML — bias, drift, explainability, NIST AI RMF.

Step — Apply

Browse live Model Risk Manager roles on GRC Careers. Related titles to search: Model Risk Manager, Model Validation Lead, AI/ML Risk Manager, Quantitative Risk Manager.