Credit manager

Times Internet · Noida, India

About Times Internet:

At Times Internet, we build premium digital products that simplify and enhance the everyday lives of people. We are India’s largest digital products company with a presence in a wide range of categories across news, entertainment,marketplaces, and transactions. Many of our products are market leaders & iconic brands in their own right. TOI, ET, Indiatimes , NBT, ET Money, Tech Gig , and Cricbuzz, among others, are products that bring you closer to your interests and aspirations. We are excited by new

possibilities and look forward to bringing new products, ideas, and technologies that help people make the most of every day. Build a career of purpose & passion with Times Internet.

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About Role

You will not inherit a credit policy, you will write it from scratch. Every decision about who we lend to, how we price it, and how we protect the portfolio starts with you. The scorecard, the fraud rules, the underwriting SOP, the NPA policy are all yours. If the portfolio goes bad, that's on you. If it performs well, that's also you

Responsibilities Write the Credit Policy — eligibility, bureau rules, income norms, LTI/FOIR limits, negative list, credit authority matrix. Start from a blank doc Build scorecard V1 — variable selection, weights, cutoffs for approve/reject/refer. Own it through iterations as portfolio data comes in Build the risk-based pricing model — segment to risk score to rate Define the fraud rules engine and select fraud tooling (velocity checks, identity mismatch, duplicate detection) Set delinquency buckets and escalation matrix; define EWS triggers before the first loan goes bad, not after Own manual underwriting for borderline cases and define the rejection reason taxonomy Define NPA resolution and foreclosure workflow — and make sure Collections can actually execute it

Requirements 6–8 years in credit risk, specifically in personal/consumer lending at an NBFC, bank, or fintech Built a scorecard before — logistic regression, decision tree, or ML-based Deep on bureau data (CIBIL, Experian, CRIF) and comfortable reading bureau API outputs Have a real view on fraud typologies in digital lending and what actually works to catch them Know RBI IRACP norms, FOIR/LTI guidelines, and Fair Practices Code — not just heard of them Comfortable in Excel/SQL; Python or R a genuine plus