Senior Model Risk Manager - AI/ML

Responsibilities

• Model Governance & Monitoring Oversight
• Maintain and enhance Mercury’s model governance framework, including inventory standards, documentation templates, validation standards, and issue management.
• Assess whether first-line monitoring efforts are effective, proportionate to model risk, and sufficient to keep models fit for purpose over time.

• Model Validation
• Perform independent validation across predictive ML models, generative AI systems, and agentic workflows, covering data, assumptions, methodology, testing, and monitoring.
• Assess risks in LLM-powered applications, including RAG pipelines, tool use, autonomy boundaries, human oversight, and hallucination risk.
• Identify and document model limitations, failure modes, and emerging AI risks including drift, instability, fairness, and robustness concerns.

• MRM Advisory
• Serve as a trusted advisor to data scientists, engineers, product teams, and risk partners throughout the AI/ML lifecycle to provide practical guidance on model risk, governance expectations, and control design without slowing responsible innovation.
• Evaluate new AI use cases for regulatory implications, materiality, and governance requirements prior to deployment.
• Help shape Mercury’s responsible AI standards, including explainability, bias assessment, testing, human oversight, and documentation.

• AI Enablement for MRM
• Develop and maintain AI-enabled automation tools to improve the speed, scale, and effectiveness of model governance and validation workflows.
• Modernize the MRM function to operate effectively in a fast-moving AI environment while maintaining strong governance standards.

Qualifications

• 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.