Risk Manager

Jupiter; Bengaluru; On-site; INR 3,500,000 - 7,500,000; Full time

Job summary

Jupiter in Bengaluru is seeking a data-driven Manager – Risk & Analytics to lead credit risk strategy across personal loans, BNPL, credit cards, and other lending products. You will design underwriting rules, monitor portfolio health, and drive analytics-led risk decisions in a fast-growing fintech environment.

The role requires 5+ years in credit risk analytics, strong SQL/Python skills, and a track record in ML-based modeling.

Qualifications

• 5+ years of experience in credit risk analytics and underwriting strategy.
• Proficient in SQL and Python for large-scale data analysis.
• Experience with predictive/ML-based risk models.
• Knowledge of bureau data, transactional data, and alternative data.
• Strong model validation and explainability skills.

Responsibilities

• Own end-to-end credit risk strategy for multiple lending products.
• Design underwriting framework, policy rules, and risk-based pricing.
• Monitor portfolio health using PD/LGD metrics and loss curves.
• Lead AI/GenAI-led risk innovations and governance.
• Mentor analysts and collaborate with product, engineering, and compliance.

Skills

SQL

Python

Credit risk analytics

Underwriting strategy

ML modeling

Education

Engineering degree (IIT/NIT or equivalent)

Tools

GenAI tooling

Statistical software

Job description

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.

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 seeking a data-driven Manager – Risk & Analytics with 5+ years of strong experience in credit risk analytics, underwriting strategy, and advanced modelling to lead and scale our risk capabilities across multiple lending products — and to help build the next generation of AI-native credit decisioning at Jupiter.This is a high-impact leadership role sitting at the intersection of Risk, Analytics, Product, and Business. You will own end-to-end credit risk strategy — from underwriting design and portfolio monitoring to advanced predictive modelling, GenAI-augmented decisioning, and policy governance — directly influencing credit losses, growth efficiency, customer experience, and long-term portfolio quality. You will lead complex analytical initiatives, mentor analysts/data scientists, and act as a trusted advisor to senior stakeholders while shaping the evolution of our credit decisioning systems — including our move toward agentic and LLM-assisted underwriting — in a high-growth environment.

Roles and responsibilities

1. Credit Risk Strategy & Portfolio Ownership

• Own and evolve credit risk strategy across Personal Loans, BNPL, credit cards, and other unsecured lending products
• Lead underwriting framework design including policy rules, score cut-offs, limit assignment logic, and risk-based pricing
• Monitor portfolio health using vintage analysis, roll rates, PD/LGD trends, early delinquency indicators, and loss curves
• dentify emerging risks, adverse selection, and structural weaknesses using statistical and ML-driven approaches
• Translate analytical insights into clear, actionable recommendations for Product, Business, and Leadership teams

2. Predictive Modelling & Advanced Analytics

• Lead development, validation, and deployment of credit risk models, including
• Probability of Default (PD)
• Early Delinquency / First EMI Default
• Drive feature engineering using bureau data, transactional behavior, alternative data (device/digital footprint, Account Aggregator data), and lifecycle signals
• Guide and review ML approaches (logistic regression, tree-based models, gradient boosting, etc.) with a focus on explainability and business alignments
• Define and oversee model performance and stability monitoring using AUC, KS, Gini, PSI, back-testing, and drift metrics
• Recommend model recalibration, retraining, or strategic overlays based on portfolio performance

3. Credit Analytics, Measurement & Experimentation

• Optimize approval rates, risk-adjusted returns, customer profitability, and portfolio ROI through deep data analysis
• Define, track, and review key credit KPIs: DPD metrics, NPA rates, loss rates, approval efficiency, and cohort performance
• Design and evaluate policy and model experiments using controlled testing and cohort analysis

4. AI & GenAI-led Risk Innovation

• Contribute to the design of AI-native underwriting systems — combining bureau, alternate data, and conversational/LLM-based signal extraction
• Explore GenAI use cases across borrower persona prediction, boundary/edge-case resolution, and policy-document reasoning for credit decisioning
• Partner with Data Science/Engineering to responsibly evaluate LLM-assisted underwriting agents — balancing model performance, explainability, and regulatory expectations
• Help define guardrails, monitoring, and governance for AI/ML systems used in credit decisions, in line with evolving regulatory guidance (e.g. RBI Model Risk Management expectations

5. Credit Policy, Governance & Controls

• Lead continuous improvement of credit policies across onboarding, pricing, limits, and lifecycle management
• Ensure strong documentation of models, policies, assumptions, and decision frameworks
• Support regulatory audits, model governance forums, and internal risk review
• Mentor and guide analysts/data scientists; set analytical standards
• Partner closely with Product, Engineering, Data Science, Operations, and Compliance teams
• Provide credit risk leadership during new product launches, feature rollouts, and strategic experiments
• Translate complex risk and fraud insights into business-friendly narratives for senior stakeholders

What we're lookingforCore requirements

• 5+ years of experience in credit risk analytics, underwriting strategy, or portfolio risk management
• Strong hands-on expertise in SQL and Python for large-scale data analysis and model
• lingProven experience building and deploying predictive / ML-based credit risk modles
• delsDeep understanding of bureau data, transactional data, and alternative data souces
• Strong grounding in model validation, stability monitoring, and explainabi
• Strong capabilities to support AI-native risk systems
• Excellent structured problem-solving and analytical rigorogor
• Ability to clearly communicate insights to non-technical and senior stakeholores
• Strong ownership mindset with the ability to operate in fast-paced, high-growth environments

Preferred

• Prior experience scaling credit systems or underwriting platforms in fintech or high-growth lending businesses
• Exposure to multiple product types (PL, Cards, BNPL, LAP, Secured Lending
• Working knowledge of Generative AI / LLMs for risk and underwriting use cases — e.g. prompt engineering, LLM-assisted decisioning or agentic workflows, RAG-based policy/document reasoning, or evaluating LLM outputs for credit and fraud use cases
• Familiarity with AI/ML governance and responsible-AI practices in a regulated (BFSI) context
• Academic background from IIT / NIT or equivalent, with strong hands-on analytical depth