Senior Model Risk Manager – AI/ML

Mercury

Senior Model Risk Manager responsible for defining AI/ML model governance. Engaging with teams to shape rigorous MRM in the context of AI at Mercury.

Posted 5/27/2026full-timeRemote • California, New York, Oregon • 🇺🇸 United StatesSenior💰 $200,700 - $250,900 per yearWebsite

Tech Stack

Tools & technologies

PythonScikit-LearnSQL

About the role

Key responsibilities & impact

Define model governance for AI/ML at Mercury.
Continuously build and enhance the frameworks for validation, monitoring, and governance.
Own validation, monitoring, and governance of Mercury’s AI/ML model portfolio.
Partner closely with data scientists, engineers, compliance leads, and product teams.
Shape Mercury’s approach to model risk management in the context of AI.
Perform independent validation across predictive ML models and generative AI systems.
Assess risks in LLM-powered applications and identify model limitations.
Serve as a trusted advisor throughout the AI/ML lifecycle.
Help shape responsible AI standards including explainability and bias assessment.
Develop AI-enabled automation tools and modernize the MRM function.
Champion MRM as a strategic enabler for AI/ML adoption across teams.

Requirements

What you’ll need

Bachelor's degree in a quantitative field (e.g. Computer Science, Engineering, Statistics, Mathematics, etc.) with 6-10 years of meaningful hands-on experience developing or validating AI/ML models and systems, ideally in financial services or fintech.
Strong technical foundations in Python, SQL, and modern ML tooling (e.g. scikit-learn, XGBoost);
Familiarity with LLMs, RAG systems, prompt engineering, and AI agent frameworks.
Experience in evaluating and testing machine learning models (e.g. in fraud detection) and generative AI systems, including custom evals, red-teaming, or frameworks.
Solid understanding of model risk governance principles and regulatory expectations (e.g. SR 11-7 / OCC 2011-12, SR 26-2).
Deep appreciation of disciplined model governance and independent effective challenge.
A healthy dose of skepticism combined with a constructive, solution-oriented approach.
Comfort operating in ambiguity: capable of synthesizing fragmented technical, operational, and business context into a clear understanding of how complex models and AI systems actually work, and making sound judgments even without a complete playbook or perfect documentation.
High agency and adaptability: able to operate effectively in a fast-moving environment where priorities evolve quickly, new ad hoc problems emerge regularly, and role boundaries are intentionally broad. You can operate effectively without tightly-defined scope, find the highest-leverage work, and get it done.
Exceptional attention to detail across documentation, code base, testing artifacts and quantitative analysis.
Strong written and verbal communication skills; you can explain model risk to a data scientist and to a regulator, and use different language for each.

Benefits

Comp & perks

Base salary
Equity
Benefits

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Hard Skills & Tools

PythonSQLscikit-learnXGBoostmachine learning modelsgenerative AI systemsmodel validationmodel risk governancefraud detectionprompt engineering

Soft Skills

attention to detailstrong communication skillsadaptabilityoperating in ambiguitysolution-oriented approachhigh agencyconstructive skepticismeffective challengesynthesis of complex informationcollaboration