Quantitative Analytics Manager, Affirm Bank Model Governance

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. We’re looking for an intelligent, driven professional to join our Bank 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. 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 Bank by mitigating risks due to ineffective model design or model misuse. What You ll Do Full-Stack Model Validation: Conduct rigorous, independent validations of sophisticated credit/fraud models, including machine learning and traditional statistical models, focusing on conceptual soundness, data integrity, and performance stability. Advanced Quantitative Monitoring: Develop automated, independent monitoring suites in Python to track KRI/KPI drift, population stability (PSI), and feature importance shifts in real-time. Remediation and Technical Advisory: Partner with 1st-line Model Developers to drive the remediation of validation findings, ensuring models and strategies are not only compliant but mathematically robust. Audit and Regulatory Liaison: Partner with Internal Audit, Internal Controls, and Compliance to facilitate the timely resolution of audit and regulatory requests. Affirm Bank: Work for the internal Bank team to support the build out of the Bank Model Risk Management function. Support the model validation requirements for Bank owned models. What We Look For 7+ years of professional experience in a highly technical capacity, such as Credit/Fraud/Financial Risk Modeling, Model Validation, or Quantitative Analytics Deep understanding of the consumer credit lifecycle and/or fraud detection. Technical familiarity with loss forecasting/fraud prediction, and stress-testing frameworks. Expert-level proficiency in Python (specifically pandas, scikit-learn, statsmodels) for replicative modeling and backtesting. Mastery of SQL for wrangling large-scale, distributed datasets and performing complex data lineage audits. A natural problem-solver with a meticulous eye for detail, a deep curiosity for how strategies perform, and sharp critical-thinking skills. Exceptional interpersonal and communication skills, with a proven ability to translate complex technical ideas