Senior Credit Risk Analyst

About Company:

India's booming consumer market has over 300 million credit-eligible consumers, yet only 35million actively use credit cards. At Snapmint, we are building a better alternative to credit cards that lets consumers buy now and pay later for a wide variety of products, be it shoes, clothes, fashion accessories, clothes or mobile phones. We firmly believe that an enduring financial services business must be built on the bedrock of providing honest, transparent and fair terms.

Founded in 2017, today we are the leading online zero-cost EMI provider in India. We have served over 10M consumers across 2,200 cities and are doubling year on year.. Our founders are serial entrepreneurs and alumni of IIT Bombay and ISB with over two decades of experience across leading organizations like Swiggy, Ola, Maruti Suzuki and ZS Associates before successfully scaling and exiting businesses in patent analytics, ad-tech and bank-tech software services

About the Role:

The Credit Risk Analyst owns the analytical foundation of the credit portfolio, translating raw transaction and customer data into actionable insights that drive approval optimization, risk calibration, and customer profitability. This role sits at the intersection of risk management, product strategy, and data engineering, directly influencing credit decisioning, policy setting, and portfolio performance for a high volume digital lending platform processing 1M+ daily transactions. Unlike traditional credit risk analysts who focus on model building, this role emphasizes portfolio observability, cohort-based performance tracking, and real-time decisioning optimization across new and repeat user segments. You will own metrics that product, risk, and business teams depend on for decision-making—and you'll have the autonomy to challenge policy assumptions with data.

Key Responsibilities:

Core Competencies & Success Metrics Domain Expertise

• Fintech: Familiarity with instant approval decisioning, high-velocity transaction flows, chargeback/fraud dynamics, and customer acquisition models.
• 4-5 years in credit risk analytics, lending analytics, or consumer fintech metrics (e.g., fraud, chargeback, risk).
• Credit & Risk Knowledge: Understanding of bureau scoring

Technical Skills

• SQL Advanced: Write complex nested queries, window functions, and multi-stage aggregations. Optimize for performance on billion-row datasets.
• Python for Analytics: Pandas, NumPy, SciPy for ad-hoc cohort analysis, statistical tests (Chisquare, t-test, survival analysis), and simple predictive models (logistic regression).

Key Performance Indicators

• Approval Optimization: Month-on-month approval rate improvement (target: +1–2% quarterly without degrading quality), funnel conversion lift from A/B tests.
• Insights Impact of policy recommendations adopted and their lift (e.g., Tightened decision rule for 1st-time users, reduced 90+ DPD by 30 bps).