Model Risk Manager

Mercury; United States; Remote; USD 110,000 - 170,000; Full time

Benefits offered by this job

Health benefits

401(k) matching

Job summary

Mercury is seeking a Model Risk Manager to build and operate a practical, risk-based Model Risk Management program while scaling toward regulated bank status.

You will conduct validations, challenge assumptions, and manage model inventory, monitoring, and governance across Risk Ops, Data, Engineering, Product, Finance, Compliance, and Internal Audit.

Qualifications

• Experience performing model validations across financial risk domains.
• Ability to challenge model developers and owners credibly.
• Familiarity with governance and risk management in regulated environments.
• Strong collaboration with risk, data, engineering, and QA teams.

Responsibilities

• Conduct independent validations of internal and third-party models.
• Evaluate methodology, data quality, and model performance.
• Maintain model inventory with ownership and risk ratings.
• Develop monitoring plans and assess model drift.
• Document conclusions and remediation actions.
• Provide guidance on limited use when validation is pending.
• Communicate findings to 1LOD and stakeholders.

Skills

Model risk management

Independent validation

Regulatory alignment

Data quality

Backtesting

Stakeholder management

Quantitative analysis

Python

SQL

Tools

Python

SQL

R

SAS

Job description

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. Showing up as a true collaborator, advisor and partner to the business will be key as we continue to build out this function and work towards our collective goals at Mercury. You should be comfortable operating in a fast-moving environment where the objective is not governance for its own sake, but helping Mercury use models safely, responsibly, and effectively.

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 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 ris