Quantitative Analytics Manager, Model Governance

What you'll do

• Conduct independent validations of credit and fraud models to ensure conceptual soundness and data integrity.
• Develop automated Python monitoring suites to track KRI/KPI drift and population stability in real-time.
• Partner with model developers to remediate validation findings and ensure mathematical robustness.
• Facilitate the timely resolution of audit and regulatory requests with internal control teams.
• Support the build-out of the Bank Model Risk Management function for internal bank models.
• Perform replicative modeling and backtesting using Python and SQL on large-scale datasets.

What we're looking for

• 7+ years of professional experience in 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, and statsmodels, for replicative modeling and backtesting.
• Mastery of SQL for wrangling large-scale, distributed datasets and performing complex data lineage audits.
• Exceptional interpersonal and communication skills to translate complex technical ideas for any audience.
• Proven ability to conduct independent validations of machine learning and traditional statistical models.