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Mercury

Runs online business banking for startups

Model Risk Manager

Quantitative RiskConfirmed live in the last 24 hours
Full-timeRemote

About the Job

Requirements

  • At least 5 years of relevant experience in model validation, model development, quantitative risk analytics, or a related discipline within banking, fintech, financial services, or consulting.
  • A bachelor's degree in Statistics, Mathematics, Physics, Computer Science, Engineering, Financial Engineering, or a related field is required.
  • Strong knowledge of model risk management principles and current regulatory expectations, including the revised interagency guidance reflected in SR 26-2.
  • Extensive experience validating or developing models in areas such as BSA/AML, fraud, financial forecasting, CECL, capital, liquidity, and credit underwriting, using methodologies such as machine learning (including XGBoost and random forests), scorecards, complex vendor models (including Alloy and Firco), and complex spreadsheet-based models.
  • Proficiency with SQL and Python for data analytics, and ability to independently build understanding of models by collecting evidence and performing data analytics.
  • Ability to explain model risk to data scientists and regulators, translate findings and recommendations into guidance for first-line-of-defense product and business teams, and adapt communication accordingly.
  • Exceptional attention to detail across documentation, code bases, testing artifacts, and quantitative analysis provided by first-line-of-defense teams.
  • Sound judgment and a practical, risk-based mindset, including ability to distinguish significant model risk from lower-risk matters and constructively challenge model owners, including senior stakeholders.
  • High ownership, intellectual curiosity, and ability to build processes and operate effectively in a fast-evolving environment with changing priorities, frequent ad hoc requests, broad role boundaries, and limited predefined scope.

Responsibilities

  • 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 proportionate to model risk and materiality.
  • Provide credible and constructive challenges to model developers, owners, and users while maintaining effective working relationships with first-line-of-defense teams.
  • Assess model risk and materiality based on each model’s inherent risk, purpose, exposure, use, and potential impact.
  • 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 third-party analytical products, including their documentation, transparency, performance, limitations, customization, and Mercury’s ability to monitor and control their use.
  • Document validation conclusions, identify issues, recommend proportionate remediation, and track findings through resolution.
  • Support controlled model use when validation cannot be completed before implementation by helping establish limitations, compensating controls, monitoring, and approvals.
  • Develop reporting on model risk, validation coverage, performance concerns, concentrations, dependencies, exceptions, and overdue remediation for management and governance committees.
  • 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 Data and AI Governance teams to define boundaries and handoffs between traditional model risk management and 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.

Desired Qualifications

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

About the Company

Mercury

Runs online business banking for startups

Mercury runs online banking for startups and small businesses, pairing business accounts and corporate cards with built-in software for bill pay, expense tracking, and bookkeeping. It rose to prominence in 2023, when thousands of startups fleeing the collapse of Silicon Valley Bank moved their cash to Mercury within days. Customers open accounts online and use one dashboard to wire money, give employees cards, pay invoices, and invest spare cash in money market and bond funds. Mercury makes money from interest on customer deposits and from card fees, along with paid treasury and software plans. It was founded in 2017 and is based in San Francisco.

Company size
1,001-5,000
Company stage
Series D
Total funding
$750.9M
Headquarters
San Francisco, CA
Founded
2017

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