Credit Manager

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, TechGig , and Cricbuzz, among others, are products that bring you closer to your interests and aspirations. We are excited by newpossibilities 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.

Website :

About RoleYou 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

ResponsibilitiesWrite the Credit Policy - eligibility, bureau rules, income norms, LTI/FOIR limits, negative list, credit authority matrix. Start from a blank docBuild scorecard V1 - variable selection, weights, cutoffs for approve/reject/refer. Own it through iterations as portfolio data comes inBuild the risk-based pricing model - segment to risk score to rateDefine 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 afterOwn manual underwriting for borderline cases and define the rejection reason taxonomyDefine NPA resolution and foreclosure workflow - and make sure Collections can actually execute it

Requirements6-8 years in credit risk, specifically in personal/consumer lending at an NBFC, bank, or fintechBuilt a scorecard before - logistic regression, decision tree, or ML-basedDeep on bureau data (CIBIL, Experian, CRIF) and comfortable reading bureau API outputsHave a real view on fraud typologies in digital lending and what actually works to catch themKnow RBI IRACP norms, FOIR/LTI guidelines, and Fair Practices Code - not just heard of themComfortable in Excel/SQL; Python or R a genuine plus