Model Risk Management Lead, Machine Learning
$164k - $245kAffirm
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 an intelligent, driven professional to join our Model Risk Management (MRM) team. This team seeks to establish, maintain and oversee an effective MRM framework to identify, quantify, monitor, mitigate and report on model risk throughout the company. You will have an outstanding opportunity to work cross-functionally to develop a profound understanding of models that drive critical business decisions, and add value to the company by mitigating risks due to ineffective model design or model misuse. What You’ll Do-
Perform independent challenges of machine learning models used for credit underwriting, credit risk, and/or fraud detection through thorough validation and ongoing monitoring
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Identify model weaknesses and opportunities for improvement
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Collaborate with model owners to remediate model validation findings
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Partner cross-functionally to implement and maintain the company’s MRM framework
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Partner with Internal Audit, Internal Controls, and Compliance to ensure timely resolution of audit and regulatory requests
What We Look For
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4-6 years of professional experience in related technical areas such as model development, model validation, data science
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Deep and broad knowledge and experience in machine learning modeling, credit underwriting, credit risk management, and/or fraud detection and fraud risk management
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Proven ability to work with script languages(e.g., Python) and large-scale dataset (e.g., SQL)
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Extensive experience with machine learning platforms and frameworks (e.g., scikit-learn, pyspark) and cloud-based coding environments and databases
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BS, MS, or PhD in a quantitative field such as Math, Data Science, Computer Science
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Oriented toward detail, curious about data/models/algorithms, skilled at critical thinking and problem solving
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Extraordinary interpersonal and verbal/written communication skills
Pay Grade - M
Equity Grade - 8
USA base pay range (all other U.S. states) per year: $164,000 - $224,000
#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.
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