Remote Senior Quantitative Analytics & Model Risk Lead

Affirm; New York (NY); On-site; USD 220,000 - 280,000; Full time

Benefits offered by this job

Equity rewards

Remote-first company

100% subsidized medical coverage (you+

Tech spending stipends

Job summary

Affirm is a remote-first company seeking an experienced professional to join the Bank Model Risk Management team. You will validate sophisticated credit and fraud models, develop automated monitoring in Python, and partner with cross-functional teams to remediate findings while ensuring robust, compliant risk management across the bank.

Ideal candidates bring 7+ years in technical risk modeling, deep credit/fraud knowledge, and expert Python/SQL skills.

Qualifications

• 7+ years of professional experience in technical Credit/Fraud/Financial Risk Modeling, Validation, or Quant Analytics.
• Deep understanding of consumer credit lifecycle and/or fraud detection.
• Proficiency in loss forecasting, fraud prediction, and stress-testing frameworks.
• Excellent communication with non-technical stakeholders.

Responsibilities

• Full-Stack validation of credit/fraud models, focusing on conceptual soundness and data integrity.
• Develop automated monitoring for KRI/KPI drift, PSI, and feature shifts in Python.
• Remediate validation findings with model developers to ensure robustness.
• Coordinate with Audit, Internal Controls, and Compliance on regulatory requests.
• Support the Bank Model Risk Management function for bank-owned models.

Skills

Model validation

Credit risk

Fraud detection

Data wrangling

Communication

Cross-functional

Tools

Python

SQL

pandas

scikit-learn

statsmodels

Job description

Affirm is a remote-first company seeking an experienced professional to join the Bank Model Risk Management team. You will validate sophisticated credit and fraud models, develop automated monitoring in Python, and partner with cross-functional teams to remediate findings while ensuring robust, compliant risk management across the bank.

Ideal candidates bring 7+ years in technical risk modeling, deep credit/fraud knowledge, and expert Python/SQL skills.