Manager - Model Validation & Model Risk

Role Summary

The Manager, Model Validation leads independent validation and effective challenge of ADF's credit, fraud and vendor models, and delivers validation reports to our bank partners and other stakeholders. The focus is on identifying, quantifying and escalating real model risks, and making sure each identified issue has an owner, a remediation plan and a clear residual-risk statement.

Key Responsibilities

• Independent Validation and Effective Challenge

• Performance Testing and Outcomes Analysis

• Fair Lending

• Documentation, Findings and Reporting

• Model Inventory and Governance

• Team Leadership and Collaboration

• Presenting to Banks, Regulators and Partners

• Tracking Model Inputs

Qualifications

Must Have

• Hands-on model development: 4+ years building credit, fraud or similar customer-facing ML models in a regulated financial-services environment (US bank, US lender or bank-partner fintech, or a US bank's India center), including direct work with XGBoost, or other gradient boosting and ML techniques, from feature engineering and tuning through documentation and implementation.

• Fair lending analysis: working familiarity with US fair-lending requirements (ECOA/Regulation B, disparate impact), proxy methods such as BISG, and metrics such as AIR/SMD, applied to model or strategy reviews. • 7+ years in total in model development, validation or model risk management in financial services, including 3+ years of independent validation or model risk management.

• Strong knowledge of US model risk guidance (SR 26-2 and its predecessor, SR 11-7) and a track record of delivering against it.

• Advanced Python and SQL on large datasets.

• Validation reports and findings that have held up under review by a bank, regulator, auditor or second line.

• Excellent written and spoken English, with the ability to explain complex technical topics to non technical and executive audiences.

• Bachelor's/Master's degree in Statistics, Mathematics, Economics, Computer Science, Data Science or a related field.

Preferred

• US consumer lending experience, especially unsecured personal loans or near-prime/subprime credit.

• Direct experience presenting to a US sponsor bank, regulator or external auditor.

• Experience validating vendor and bureau models and running decision-engine implementation testing.

• Experience with cash-flow or bank-transaction data in underwriting.

• Experience with Spark, SAS or AWS-based data platforms.

• 2+ years of people leadership in a quantitative team.