Job Title

What we do:Money. It's always on our mind and often comes with a rollercoaster of emotions and complex jargon. That’s why at Jupiter, our mission is to improve your financial well-being by giving you full control over your money, helping you track, save, and invest with confidence.We’re a financial services platform that uses technology to simplify money management. Whether it’s a savings account, payments, loans, credit cards, investments, or smart money tools it’s all on Jupiter. We break down banking jargon, offer spending insights, and give users modern features to make better financial decisions.Our journey:Jupiter was founded in 2019 by Jitendra Gupta (founder of Citrus Pay), who saw how broken personal finance felt compared to customer-first experiences like food or entertainment. We launched in 2021 with a 100,000+ waitlist. Today, 30 Lakh+ users trust us with their money.We've built a team of creative thinkers and domain experts, driven by a shared vision of a transparent and inclusive financial ecosystem.We’ve embraced cutting- edge technology with high ownership and deep customer obsession. Our team, spanning Mobile, Platform, Data, AI & ML — is building to scale products across the board. From AI to behavioral science, we’re creating world class banking experiences, and we’re looking for more builders to join us.Who we're looking for:We are looking for a data-first Senior Credit Risk Analyst with 3-6 years of hands-on experience in credit risk analytics and strong expertise in predictive modeling and machine learning to strengthen our underwriting, portfolio monitoring, and risk strategy across various lending products.This role sits at the intersection of risk, analytics, product, and business, and will directly influence credit losses, growth efficiency, customer experience, and long-term portfolio quality.A significant part of your time will be spent working deeply with data in identifying patterns, testing hypotheses, building insights, training algorithms and predictive models and influencing credit policy, underwriting logic, and product decisions.Roles and Responsibilities: Credit Risk Strategy & MonitoringAct as a key contributor to credit risk strategy and underwriting decisions within defined policy frameworksMonitor credit risk performance across Personal Loans, Credit Cards, BNPL, LAP, and other secured/unsecured productsIdentify emerging risk trends, early delinquency signals, and adverse selection using statistical and ML-driven analysisDesign and refine underwriting rules, score cut-offs, limit assignment logic, and pricing strategiesConduct deep dives on vintage curves, roll rates, PD/LGD trends, and loss driversConvert analytical and model outputs into clear recommendations for policy and product teamsPredictive Modeling & Machine Learning Build, validate, and deploy predictive credit risk models such as:Probability of Default (PD)Early Delinquency / First EMI DefaultLimit assignment and line managementRisk-based pricing and segmentationPerform feature engineering using bureau data, transactional behavior, alternative data, and customer lifecycle signalsSupport development and tuning of ML models (logistic regression, tree-based models, gradient boosting, etc.)Evaluate model performance using AUC, KS, Gini, PSI, stability metrics, and back-testingMonitor model health post-deployment and recommend retraining, recalibration, or strategy overlaysWork closely with Data Science teams to ensure models are business-aligned, explainable, and production-readyAnalytics & InsightsOwn end-to-end credit analytics: data extraction → modeling / insight → strategy recommendation → post-impact measurementAnalyze large datasets to optimize approval rates, risk-adjusted returns, loss curves, and customer-level profitabilityBuild and track credit KPIs such as DPD metrics, NPA rates, loss rates, approval efficiency, and ROI by segmentQuantify policy and model impact through controlled experiments and cohort analysisCredit Policy, Controls & Portfolio ManagementRecommend improvements to credit policies across onboarding, limit management, pricing, and lifecycle strategiesConduct root-cause analysis for portfolio deterioration, model drift, or unexpected loss spikesDocument model logic, credit policies, assumptions, and decision frameworksSupport regulatory audits, model governance, and internal risk reviewsCross-Functional CollaborationWork closely with Product, Engineering, Data Science, Operations, and Compliance teamsProvide credit risk inputs during new product launches, feature changes, and experimentsTranslate fraud insights into clear business recommendationsWhat is needed for this role:3–6 years of experience in credit risk analytics, underwriting strategy, or portfolio riskStrong hands-on experience with advanced proficiency in SQL and Python for data analysis and modelingPreferably have experience in building predictive / ML-based credit modelsAdvanced proficiency in SQL and Python for data analysis and modelingExperience with bureau data, transactional data, and alternative data sourcesSolid understanding of model validation, stability monitoring, and explainabilityStrong analytical rigor and structured problem-solving ability(IIT/NIT or equivalent hands-on data depth preferred)Ability to communicate model insights clearly to non-technical stakeholders