Model Risk Manager

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About This Role

Mercury seeks a Model Risk Manager to develop and run a practical, risk-based Model Risk Management function as the organization scales and prepares for regulated banking. In this remote, full-time role, you will provide independent oversight and constructive challenge throughout the model lifecycle across domains such as credit underwriting, fraud prevention, BSA/AML, CECL, finance, liquidity, pricing, and operational risk. This position suits an analytical professional who can balance technical depth…

Job Description

Responsibilities

• Conduct independent validations and reviews for internal and third-party models spanning credit, fraud, BSA/AML, CECL, finance, liquidity, pricing, and operational risk.
• Evaluate methodologies, assumptions, data lineage, implementation quality, and performance via sensitivity testing, benchmarking, back-testing, and outcomes analysis.
• Deliver credible challenges to model creators, owners, and users while preserving effective working relationships with the first line of defense.
• Maintain a comprehensive model inventory that tracks ownership, risk ratings, dependencies, limitations, validation status, and open issues.
• Review ongoing monitoring plans and results to detect overrides, data drift, performance thresholds, and triggers that render models unfit for purpose.
• Assess vendor models and third-party analytical solutions regarding documentation transparency, performance, limitations, and control capabilities.
• Document validation findings, formulate remediation recommendations, and track issues through to final resolution.
• Assist management by establishing temporary limitations, monitoring, and compensating controls for models implemented prior to full validation.
• Create reports for governance committees covering model risk, validation coverage, concentrations, dependencies, and overdue remediation items.
• Contribute to updates for Mercury policies, standards, procedures, and templates for model risk management.
• Leverage automation and analytical tools to streamline inventory management, testing, monitoring, and reporting workflows.
• Collaborate with Data and AI Governance teams to set boundaries between traditional model risk management and advanced analytical systems like generative AI.
• Participate in regulatory examinations, Internal Audit reviews, and related assurance activities.

Requirements

• At least 5 years of relevant professional experience in model validation, quantitative risk analytics, or model development within fintech, banking, financial services, or consulting.
• Extensive experience validating or developing models in areas such as BSA/AML, fraud, financial forecasting, CECL, capital, liquidity, or credit underwriting.
• Familiarity with methodologies including machine learning techniques like XGBoost and random forests, scorecards, complex vendor tools like Alloy and Firco, and spreadsheet models.
• Demonstrated knowledge of model risk management principles and regulatory expectations such as revised interagency guidance SR 26-2.
• Proficiency in SQL and Python applied from a data analytics perspective.
• Strong written and verbal communication skills tailored to varied audiences from data scientists to regulators and business teams.

Qualifications

• Bachelor’s degree in Statistics, Mathematics, Physics, Computer Science, Engineering, Financial Engineering, or a related field.
• Master’s degree or PhD is preferred.
• Prior experience helping build or significantly enhance a model risk management program.
• Familiarity with bank regulatory examinations, charter readiness, or risk program implementation inside growing financial institutions.
• Knowledge of Haskell.

Core Skills

Benefits

• Base salary compensation
• Equity including stock options and RSUs
• Company benefits package

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