Underwriting/Credit Model Risk Senior Manager - Machine Learning

Affirm · Remote · US, Canada only

US, Canada only9+ years required$255K–315K/yrOpen 3 monthsLooks fresh

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First recorded by Psst: 12 October 2026. Sign in with Google to continue to the employer. A free account is enough.

About the job

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most.

About Enterprise Risk & Internal Audit

The Enterprise Risk & Internal Audit department protects Affirm’s operations and long-term success. We identify, assess, and monitor risk across the business. Our work includes internal audits, regulatory assurance, fraud prevention, and crisis planning. The team maintains strong controls, tracks key risk indicators, and ensures readiness for external reviews. We uphold accountability, transparency, and resilience to support responsible growth.

About the Team

We're looking for a sharp, driven quantitative professional to join our dynamic Model Risk Management (MRM) team. In this role, you will oversee the underwriting and credit risk validation sector - the most critical pillar of Affirm’s MRM framework. This is a unique position designed for a seasoned technical leader who can build, mentor, and manage a high-performing team while remaining deeply hands-on, personally executing validations of our most complex machine learning models.

You'll collaborate across the company to shape a best-in-class risk framework, ensuring our models are robust, reliable, and ready for the future. If you're passionate about the intersection of finance, credit risk, and data science, this role is for you.

What You'll Do

  • Sector Leadership & Team Management: Oversee the underwriting and credit risk validation sector. Lead, mentor, and manage a team of talented quantitative professionals while setting the strategic direction for this critical risk domain.
  • Hands-On Model Validation: Execute rigorous, independent validations of sophisticated machine learning models across underwriting and credit risk management.
  • Technical Review: Evaluate conceptual soundness, data integrity, and performance stability through comprehensive testing, in-depth reviews, and continuous monitoring to ensure model accuracy.
  • Identification & Remediation: Detect critical model weaknesses and identify areas for improvement; collaborate with model owners and developers to track and resolve validation findings.
  • Framework Enhancement: Partner with cross-functional stakeholders to implement and refine the enterprise-wide Model Risk Management (MRM) framework, ensuring it remains robust and innovative.
  • Audit & Compliance: Coordinate with Accounting, Internal Controls, and Compliance teams to ensure all model activities meet or exceed internal standards and regulatory expectations.

What We Look For

  • Experience: 9+ years of professional experience in a highly technical role, such as model development, model validation, or quantitative analytics.
  • Domain Expertise: Deep understanding of machine learning modeling, underwriting, and credit risk management.
  • Technical Proficiency: Advanced ability to script in Python and expertise in using SQL to manipulate and analyze large-scale, complex datasets.
  • Leadership & Influence: Proven track record of hiring, managing, and mentoring quantitative teams. Ability to transition seamlessly between high-level strategic oversight and detailed, hands-on technical work.
  • Education: A BS, MS, or PhD in a quantitative discipline such as Statistics, Quantitative Finance, Mathematics, or Data Science.
  • Analytical Mindset: A natural problem-solver with a meticulous eye for detail, a deep curiosity regarding model mechanics, and sharp critical-thinking skills.
  • Communication: Exceptional interpersonal skills with a proven ability to distill and translate complex technical concepts for diverse audiences.

Pay Grade - Q

Equity Grade - 14

Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.)

USA base pay range (CA, WA, NY, NJ, CT) per year: $255,000 - $315,000

USA base pay range (all other U.S. states) per year: $227,000 - $287,000

#LI-Remote

Remote-first with flexibility built in

Affirm is proud to be a remote-first company. Most roles can be done from almost anywhere within the country of employment. Some positions may occasionally require in-person work at an Affirm office, and a few are office-based due to the nature of the work. All new hires will be invited to attend an in-person onboarding experience.

Benefits designed for you

Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights:

  • Health coverage at no cost: We cover 100% of premiums for employees and their dependents.
  • Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options that are right for you.
  • Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to.
  • Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount.

We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help.

For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law.

By clicking "Submit Application," you acknowledge that you have read Affirm's  Global Candidate Privacy Notice and consent to the use of your personal information as described.

Apply on Affirm's site

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