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 & Monitoring
- Act as a key contributor to credit risk strategy and underwriting decisions within defined policy frameworks
- Monitor credit risk performance across Personal Loans, Credit Cards, BNPL, LAP, and other secured/unsecured products
- Identify emerging risk trends, early delinquency signals, and adverse selection using statistical and ML-driven analysis
- Design and refine underwriting rules, score cut-offs, limit assignment logic, and pricing strategies
- Conduct deep dives on vintage curves, roll rates, PD/LGD trends, and loss drivers
- Convert analytical and model outputs into clear recommendations for policy and product teams
Predictive Modeling & Machine Learning
- Build, validate, and deploy predictive credit risk models such as:
- Probability of Default (PD)
- Early Delinquency / First EMI Default
- Limit assignment and line management
- Risk-based pricing and segmentation
- Perform feature engineering using bureau data, transactional behavior, alternative data, and customer lifecycle signals
- Support 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-testing
- Monitor model health post-deployment and recommend retraining, recalibration, or strategy overlays
- Work closely with Data Science teams to ensure models are business-aligned, explainable, and production-ready
Analytics & Insights
- Own end-to-end credit analytics: data extraction modeling / insight strategy recommendation post-impact measurement
- Analyze large datasets to optimize approval rates, risk-adjusted returns, loss curves, and customer-level profitability
- Build and track credit KPIs such as DPD metrics, NPA rates, loss rates, approval efficiency, and ROI by segment
- Quantify policy and model impact through controlled experiments and cohort analysis
Credit Policy, Controls & Portfolio Management
- Recommend improvements to credit policies across onboarding, limit management, pricing, and lifecycle strategies
- Conduct root-cause analysis for portfolio deterioration, model drift, or unexpected loss spikes
- Document model logic, credit policies, assumptions, and decision frameworks
- Support regulatory audits, model governance, and internal risk reviews
Cross-Functional Collaboration
- Work closely with Product, Engineering, Data Science, Operations, and Compliance teams
- Provide credit risk inputs during new product launches, feature changes, and experiments
- Translate fraud insights into clear business recommendations
What is needed for this role:
- 36 years of experience in credit risk analytics, underwriting strategy, or portfolio risk
- Strong hands-on experience with advanced proficiency in SQL and Python for data analysis and modeling
- Preferably have experience in building predictive / ML-based credit models
- Advanced proficiency in SQL and Python for data analysis and modeling
- Experience with bureau data, transactional data, and alternative data sources
- Solid understanding of model validation, stability monitoring, and explainability
- Strong 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
