Senior Quantitative Analytics & Model Risk Manager

Affirm; Sioux Falls (SD); On-site; USD 195,000 - 280,000; Full time

Benefits offered by this job

Health coverage

Equity/Stock rewards

Monthly stipends for health, wellness,

Technology stipend

Job summary

Affirm is building a robust Bank Model Risk Management function and seeks an experienced professional to validate complex credit and fraud models. The role involves rigorous, independent validations, real-time monitoring, and close partnership with first-line developers to drive remediation and maintain regulatory compliance.

The candidate will work with Internal Audit and Compliance to address audit requests and help ensure risk controls across the model lifecycle.

Qualifications

• 7+ years of professional experience in technical risk modeling, validation or analytics.
• Deep understanding of consumer credit lifecycle and/or fraud detection.
• Proficient in loss forecasting, fraud prediction, and stress-testing frameworks.

Responsibilities

• Conduct end-to-end validation of credit/fraud models focusing on conceptual soundness and data integrity.
• Develop automated monitoring suites in Python to track drift and feature importance shifts in real-time.
• Collaborate with model developers to remediate findings and ensure mathematical robustness.
• Coordinate with Audit, Controls, and Compliance on regulatory requests.

Skills

Python (Pandas)

SQL

Model Validation

Credit/Fraud/Financial Risk

Communication Skills

Education

Bachelor's degree in a quantitative field

Tools

Git

Job description

Affirm is building a robust Bank Model Risk Management function and seeks an experienced professional to validate complex credit and fraud models. The role involves rigorous, independent validations, real-time monitoring, and close partnership with first-line developers to drive remediation and maintain regulatory compliance.

The candidate will work with Internal Audit and Compliance to address audit requests and help ensure risk controls across the model lifecycle.