Model Risk Manager - Mercury

About the Role

Mercury is looking for a Model Risk Manager to help build and operate a practical, risk-based Model Risk Management program as we continue to scale and prepare for life as a regulated bank.

Models support an increasingly broad range of decisions across Mercury, including credit underwriting, portfolio management, fraud prevention, BSA/AML, CECL, finance, liquidity, pricing, and operational processes. This role will provide independent review and effective challenge throughout the model lifecycle while helping Mercury apply governance proportionate to the actual risk of each model.

This is a hands-on role for someone who can move comfortably between technical analysis, regulatory expectations, and business context. You will conduct model validations, challenge assumptions and limitations, improve our model inventory and monitoring practices, and work closely with teams across Risk Ops, Data, Engineering, Product, Finance, Compliance, and Internal Audit.

What You’ll Do

• Perform independent validations and reviews of internally developed and third-party models across areas such as credit, fraud, BSA/AML, CECL, finance, liquidity, pricing, and operational risk.
• Evaluate each model’s methodology, assumptions, data quality and lineage, implementation, and performance through reviews and testing such as outcomes analysis, benchmarking, back-testing, and sensitivity testing scaled to its risk and materiality.
• Provide credible and constructive challenges to model developers, owners, and users while maintaining effective working relationships with 1LOD.
• Assess model risk and materiality based on a model’s inherent risk, purpose, exposure, use, and potential impact.
• Help maintain a complete and accurate model inventory, including model ownership, risk rating, dependencies, limitations, validation status, monitoring requirements, and open issues.
• Review ongoing monitoring plans and results, including performance thresholds, overrides, data drift, model changes, and conditions that could make a model no longer fit for purpose.
• Evaluate vendor models and other third-party analytical products, including available documentation, transparency, performance, limitations, customization, and Mercury’s ability to monitor and control their use.
• Document validation conclusions clearly, identify issues, recommend proportionate remediation, and track findings through resolution.
• Support controlled model use when validation cannot be completed before implementation by helping establish appropriate limitations, compensating controls, monitoring, and approvals.
• Develop clear reporting on model risk, validation coverage, performance concerns, concentrations, dependencies, exceptions, and overdue remediation for management and governance committees.
• Help improve Mercury’s Model Risk Management Policy, standards, procedures, and templates.
• Use automation and analytical tools to make model inventory management, testing, monitoring, and reporting more efficient.
• Partner with Mercury’s Data and AI Governance teams to establish clear boundaries and handoffs between traditional model risk management and the governance of generative AI, agentic AI, and other analytical systems outside the formal model definition.
• Support regulatory examinations, Internal Audit reviews, and other assurance activities involving model risk.

What We’re Looking For

• 5+ years of relevant experience in model validation, model development, quantitative risk analytics, or a related discipline within banking, fintech, financial services, or consulting.
• Bachelor’s degree in Statistics, Mathematics, Physics, Computer Science, Engineering, Financial Engineering, or a related field. Master’s degree or PhD is preferred.
• Strong knowledge of model risk management principles and current regulatory expectations, including the revised interagency guidance reflected in SR 26-2.
• Intensive experience validating or developing models in one or more areas such as BSA/AML, fraud, financial forecasting, CECL, capital, liquidity, and credit underwriting, using methodologies such as machine learning (e.g., XGBoost, random forests), scorecards, complex vendor models (e.g., Alloy, Firco), and complex spreadsheet-based models.
• Proficiency with SQL and Python from a data analytics lens; comfortable building an understanding of models independently of their developers through evidence collection and data analytics.
• Strong written and verbal communication skills; ability to explain model risk to data scientists and regulators, or translate findings into guidance for business teams.
• Exceptional attention to detail across documentation, code bases, testing artifacts, and quantitative analysis.
• Sound judgment and a practical, risk-based mindset to distinguish significant model risk from lower-risk matters and challenge stakeholders constructively.
• High ownership, intellectual curiosity, and comfort building processes in an evolving fintech environment.

Preferred Qualifications

• Experience helping build or materially enhance a model risk management program.
• Familiarity with bank regulatory examinations, charter readiness, or risk program implementation within a growing financial institution.
• Knowledge of Haskell.