Quantitative Analytics Manager, Affirm Bank Model Governance

Affirm is seeking a Quantitative Analytics Manager to join the Bank Model Risk Management team. This remote US role focuses on establishing and maintaining a comprehensive framework to identify, quantify, monitor, mitigate, and report on model risk across the organization. You will work across functions to validate models that inform critical business decisions and ensure models are mathematically sound and compliant.

Responsibilities

• Conduct independent, full-stack validations of credit and fraud models, including machine learning and traditional statistical approaches, assessing conceptual soundness, data integrity, and performance stability.
• Design and deploy automated monitoring systems in Python to track KRI/KPI drift, population stability (PSI), and feature importance shifts in real-time.
• Work with model development teams to remediate validation findings and ensure models meet mathematical and compliance standards.
• Support Internal Audit, Internal Controls, and Compliance in resolving audit and regulatory requests related to model governance.
• Support validation requirements for Bank-owned models as the Bank Model Risk Management function expands.

Requirements

• 7+ years of professional experience in credit risk modeling, fraud modeling, financial risk modeling, model validation, or quantitative analytics.
• Deep working knowledge of the consumer credit lifecycle and/or fraud detection.
• Technical proficiency with loss forecasting, fraud prediction, and stress-testing frameworks.
• Expert-level Python proficiency, including pandas, scikit-learn, and statsmodels for replicative modeling and backtesting.
• Advanced SQL skills for large-scale distributed data manipulation and data lineage auditing.

Benefits

• Base Pay Grade O; Equity Grade USA 12 (new employees typically enter at the start of the pay range).