Model Risk Manager

Job Description:

• Perform independent validations and reviews of internally developed and third-party models across credit, fraud, BSA/AML, CECL, finance, liquidity, pricing, and operational risk
• Evaluate model methodology, assumptions, data quality and lineage, implementation, and performance through outcomes analysis, benchmarking, back-testing, and sensitivity testing
• Provide credible and constructive challenges to model developers, owners, and users
• Assess model risk and materiality based on inherent risk, purpose, exposure, use, and potential impact
• Maintain a complete and accurate model inventory, including 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 fitness for purpose
• Evaluate vendor models and third-party analytical products
• Document validation conclusions, identify issues, recommend remediation, and track findings through resolution
• Support controlled model use when validation cannot be completed before implementation
• Develop reporting on model risk, validation coverage, performance concerns, concentrations, dependencies, exceptions, and overdue remediation
• Improve Mercury’s Model Risk Management Policy, standards, procedures, and templates
• Use automation and analytical tools to improve model inventory management, testing, monitoring, and reporting
• Partner with Data and AI Governance teams on boundaries between traditional model risk management and generative AI, agentic AI, and other analytical systems
• Support regulatory examinations, Internal Audit reviews, and other assurance activities involving model risk

Requirements:

• 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
• 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 areas such as BSA/AML, fraud, financial forecasting, CECL, capital, liquidity, and credit underwriting
• Experience with machine learning methodologies such as XGBoost and random forests, scorecards, complex vendor models, and complex spreadsheet-based models
• Proficiency with SQL and Python
• Strong written and verbal communication skills
• Exceptional attention to detail across documentation, code base, testing artifacts and quantitative analysis
• Sound judgment and a practical, risk-based mindset
• High ownership, intellectual curiosity, and comfort building processes in a fast evolving environment
• Master’s degree or PhD is preferred
• Experience helping build or materially enhance a model risk management program is preferred
• Familiarity with bank regulatory examinations, charter readiness, or implementation of risk programs within a growing financial institution is preferred
• Knowledge of Haskell is preferred

Benefits:

• Base salary
• Equity (stock options/RSUs)
• Benefits
• Equal Employment Opportunity employer
• Reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs