Financial Model Risk Management Lead

Overview

The Enterprise Risk & Internal Audit department protects the organization’s operations and long-term success. This team identifies, assesses, and monitors risk across the business, including internal audits, regulatory assurance, fraud prevention, and crisis planning. The group maintains strong controls, tracks key risk indicators, and supports readiness for external reviews.

This role joins the Model Risk Management (MRM) team as a Lead validator for Finance and Analytics models. MRM serves as the organization’s second line of defense by establishing and overseeing an effective model risk management framework to identify, quantify, monitor, mitigate, and report on model risk.

Responsibilities

• Perform independent challenges of financial and analytics quantitative models, including:
  • ALM
  • allowance/loss forecasting
  • loan transition models (LTM)
  • corporate financial planning models
  • take-up and engagement models
  • related decision-support analytics
  • Validate models through rigorous validation and ongoing monitoring
• Lead end-to-end validation engagements on high-complexity, high-criticality models
• Identify model weaknesses, limitations, and opportunities for improvement; communicate findings clearly to both technical and non-technical stakeholders
• Collaborate with model owners and stakeholders across Finance, Quantitative Research, and Growth Analytics to remediate validation findings and strengthen model governance
• Partner cross-functionally to implement and maintain the MRM framework
• Coordinate with Internal Audit, Internal Controls, Accounting, and Compliance to support timely resolution of audit, regulatory, and examiner requests

Requirements

• 4–6 years of professional experience in related technical areas such as model development, model validation, quantitative finance, or data analytics, with meaningful exposure to financial and/or analytics model validation
• Deep and broad knowledge of corporate finance, treasury, ALM, and/or actuarial/statistical forecasting
• Experience in credit underwriting or credit risk management is a plus
• Proven ability to work with scripting languages (e.g., Python) and large-scale datasets (e.g., SQL)
• Comfort reviewing models built in Excel, Python, Databricks, or similar environments
• Experience with statistical modeling, time-series forecasting, simulation, and/or machine learning techniques as applied to finance and analytics use cases
• BS, MS, or PhD in a quantitative field such as Quantitative Finance, Financial Engineering, Mathematics, Statistics, Economics, Computer Science, or Data Science
• Detail-oriented and intellectually curious about data, models, and business context; strong critical thinking and structured problem-solving
• Excellent interpersonal and verbal/written communication skills, including the ability to influence model owners and present to senior stakeholders while navigating cross-functional remediation

Preferred Qualifications

• Experience in credit underwriting and/or credit risk management

Compensation & Benefits

• Pay grade: M
• Equity grade: 8
• Base pay range per year: $145,000 - $205,000
• Base pay is part of a total compensation package that may include:
  • equity rewards
  • monthly stipends for health, wellness, and tech spending
  • benefits, including 100% subsidized medical coverage, dental and vision for you and your dependents
• Health care coverage: premiums covered for all levels of coverage for you and your dependents
• Flexible Spending Wallets: generous stipends for Technology, Food, various Lifestyle needs, and family forming expenses
• Time off: competitive vacation and holiday schedules
• ESPP: employee stock purchase plan enabling you to buy shares at a discount

Location

Remote-first role. The majority of roles are remote and can be worked almost anywhere within the country of employment.

For proximal roles, remote work is flexible, but occasional in-person work may be required at an assigned office. A limited number of roles remain office-based due to the nature of responsibilities.

For U.S. positions that could be performed in Los Angeles or San Francisco: pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, the employer will consider for employment qualified applicants with arrest and conviction records.