Senior Model Risk Manager; AI​/ML

Requirements

• 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.

• As Senior Model Risk Manager – AI/ML, you will define what model governance looks like for AI/ML t means continuously building and enhancing the frameworks, not just inheriting them.
• You will own validation, monitoring, and governance of Mercury’s AI/ML model portfolio, but more than that, you will be a thought leader in an industry‑wide conversation about how MRM must evolve in the context of AI.
• You will partner closely with data scientists, engineers, compliance leads, and product teams, and you will help shape not just Mercury’s approach, but set a standard for what rigorous, forward‑looking MRM on AI can look like in fintech.

• 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.

• 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.

• 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.

• 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.

• Champion MRM as a strategic enabler of safe and scalable AI/ML adoption, not simply a control function.
• Build model risk literacy across engineering, product, data science, compliance, and risk teams.