Manager - Model Validation & Model Risk

Applied Data Finance; India; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Applied Data Finance seeks a Manager of Model Validation to lead independent validation of credit, fraud and vendor models. You will challenge models, produce validation reports for bank partners, and escalate risks with remediation plans.

You will guide performance testing, governance, and documentation, collaborate with teams, and present findings to banks and regulators while ensuring clear residual-risk statements.

Qualifications

• Hands-on model development of credit, fraud or similar ML models in regulated financial services.
• Fair lending analysis familiarity (ECOA/Reg B, disparate impact) applied to reviews.
• Total 7+ years in model development/validation in financial services, including 3+ years independent validation.
• Strong knowledge of US model risk guidance SR 26-2 and related precedents.
• Advanced Python and SQL on large datasets.
• Validation reports that stood up under bank/regulator/auditor review.
• Excellent written and spoken English; ability to explain technical topics to executives.

Responsibilities

• Independent Validation and Effective Challenge.
• Performance testing and outcomes analysis.
• Documentation, findings and reporting of validation results.
• Model inventory and governance.
• Team leadership and cross-functional collaboration.
• Presenting to banks, regulators and partners.
• Tracking model inputs and updates.

Skills

Hands-on modeling

US model risk guidance

Independent validation

Python

SQL

Documentation & reporting

English communication

Education

Bachelor's/Master's in Statistics/Math/CS/Data Science

Tools

XGBoost

Python

SQL

Spark

SAS

AWS

Job description

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
• 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.