Director – Credit Risk Strategy – Consumer Lending

Function: Data Science & Modelling – ECM Credit Risk Strategy.

About the Role:

We are seeking a Director-level Credit Risk Strategy leader to own and drive the Returning / Existing Customer Management (ECM) strategy for a large consumer lending portfolio, including credit cards, personal loans, and other unsecured lending products. This is not a pure Credit Strategy role and not a pure Data Science role. We are looking for a technical Credit Risk professional who has grown into strategy and business ownership without losing technical depth – someone who can form hypotheses, interrogate models and data, design experiments, and translate analysis into credit strategy that moves the P&L.

The ideal candidate has spent the early part of their career hands-on in credit risk analytics, modelling, and machine learning, then progressively moved into Credit Risk strategy – ECM, and senior strategy leadership – while remaining technically fluent enough to challenge analysts, modelers, and data scientists.

Key Responsibilities:

Strategy & Business Ownership:

– Own the Returning / ECM / portfolio strategy for consumer lending products including credit cards, personal loans, and unsecured lending.

– Independently formulate strategy – not merely execute strategy designed by a US or global team.

– Own the end-to-end credit risk lifecycle for existing customers – from re-underwriting and offer generation through line assignment, pricing, in-life account management, and delinquency/servicing strategy – leveraging ML/AI models, alternative data, and advanced analytics to drive profitable growth while keeping credit and fraud losses within risk appetite.

– Define and evolve the Returning channel credit strategy roadmap, govern policy and rule changes end-to-end, and serve as the senior credit strategy partner to Finance, Portfolio Management, Capital Markets, Product, Engineering, Legal, Compliance, Operations, Marketing, and Fraud.

– Define and drive credit levers: eligibility, underwriting, exposure management, CLI/CLD, pricing, cross-sell / repeat lending, top-up lending, and portfolio actions.

– Translate analysis into measurable economics: vintage performance, loss rates, profitability, and risk-adjusted returns.

– Present strategy and recommendations to senior leadership with clear rationale and quantified impact.

Technical, Analytical & Modelling Depth:

– Form hypotheses, perform or direct analysis, design A/B tests, interpret results, and determine whether outcomes make sense.

– Independently interrogate models and data using Python, R, and SQL – challenge outputs when they don’t look right.

– Understand, build, or use credit and behavioral models including machine learning models (risk models, valuations, NPV, loss forecasting, classification, segmentation, and predictive models).

– Apply statistics and experimentation (champion-challenger, hypothesis testing) to credit strategy.

– Collaborate with data scientists and modelers, but retain the ability to validate their work, including ML-based models.

– Stay current with modern modelling techniques and tooling.

Leadership & Influence:

– Lead and develop a high-performing team of analysts, managers, and data scientists.

– Influence cross-functional stakeholders (risk, product, finance, operations, technology).

– Operate with Director-level judgment in ambiguous, open-ended situations.

– Balance risk reduction with revenue growth and customer experience.

Required Qualifications:

Experience:

– 10 – 15 years of total experience.

– Consumer lending background – credit cards, personal loans, and/or unsecured lending.

– Early-career hands-on experience in Credit Risk Analytics / Modelling / Machine Learning (4 – 5 years minimum).

– Subsequent progression into Credit Risk Strategy – ECM / Returning Strategy and senior strategy leadership.

– Demonstrated ownership of strategy – not just execution of strategy designed elsewhere.

Education:

– Must have Undergraduate degree in Engineering from a premier institution (IIT / NIT / NSIT / BITS preferred).

– Strong analytical background (Economics, Statistics, Mathematics, or related fields will also be considered).

Technical Skills:

– Python: Strong analytical and modelling capability (near-gating requirement).

– R: Proficiency in analytical and modelling work.

– SQL: Strong proficiency.

– Machine Learning: Understanding and applied experience with ML models (classification, segmentation, predictive modelling).

– Statistics & Experimentation: A/B testing, champion-challenger, hypothesis testing.

– Modelling: Credit risk models, behavioral models, valuations, loss forecasting, and ML-based models.

– Data fluency: Ability to independently investigate data and challenge model outputs.

Domain Skills:

– Credit levers: eligibility, underwriting, CLI/CLD, exposure management, pricing.

– Portfolio thinking: vintage, loss, profitability, risk-return.

– Existing Customer Management / Returning / repeat / top-up lending strategy.

– Credit cards, personal loans, and unsecured lending product knowledge.

– US consumer credit exposure highly desirable.