Model Risk Management Lead, Machine Learning

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

• Identify model weaknesses and opportunities for improvement

• Collaborate with model owners to remediate model validation findings

• Partner cross-functionally to implement and maintain the company’s MRM framework

• Partner with Internal Audit, Internal Controls, and Compliance to ensure timely resolution of audit and regulatory requests

What We Look For

• 4-6 years of professional experience in related technical areas such as model development, model validation, data science

• Deep and broad knowledge and experience in machine learning modeling, credit underwriting, credit risk management, and/or fraud detection and fraud risk management

• Proven ability to work with script languages(e.g., Python) and large-scale dataset (e.g., SQL)

• Extensive experience with machine learning platforms and frameworks (e.g., scikit-learn, pyspark) and cloud-based coding environments and databases