Model Risk Manager

Mercury•Portland, OR

6d•$128,600 - $160,700

About The Position

Mercury is seeking a Model Risk Manager to establish and operate a practical, risk-based Model Risk Management program as the company scales and prepares to become a regulated bank. The role involves providing independent review and effective challenge throughout the model lifecycle for models used in credit underwriting, portfolio management, fraud prevention, BSA/AML, CECL, finance, liquidity, pricing, and operational processes. This is a hands-on position requiring a blend of technical analysis, understanding of regulatory expectations, and business context. The Model Risk Manager will conduct model validations, challenge assumptions, improve model inventory and monitoring, and collaborate with various teams including Risk Ops, Data, Engineering, Product, Finance, Compliance, and Internal Audit. The ideal candidate will be a collaborator and advisor, comfortable in a fast-paced environment, focusing on the safe, responsible, and effective use of models.

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 one or more areas such as BSA/AML, fraud, financial forecasting, CECL, capital, liquidity, and credit underwriting, using one or more 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 understanding of models independently of their developers through collecting evidence and data analytics.
Strong written and verbal communication skills; you can explain model risk to a data scientist and to a regulator, or translate your findings and recommendations to guidance for the 1LOD product/business teams, and use different language for each.
Exceptional attention to detail across documentation, code base, testing artifacts and quantitative analysis provided by 1LOD.
Sound judgment and a practical, risk-based mindset: you can distinguish significant model risk from lower-risk matters and have the confidence to challenge model owners constructively, including senior stakeholders.
High ownership, intellectual curiosity, and comfort building processes in a fast evolving environment, where priorities evolve quickly, new ad hoc requests 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.

Nice To Haves

Master’s degree or PhD is preferred.
Experience helping build or materially enhance a model risk management program.
Familiarity with bank regulatory examinations, charter readiness, or the implementation of risk programs within a growing financial institution.
Knowledge of Haskell

Responsibilities

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 testings 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.