About Snapmint:

Snapmint is a leading fintech company redefining access to consumer credit in India. With over 10million customers across 2,200+ cities, Snapmint's zero-cost EMI platform enables responsible purchases without the need for a credit card, across categories including fashion, electronics, and lifestyle. India has over 300 million credit-eligible consumers, yet fewer than 35 million actively use credit cards. Snapmint bridges this gap by offering a trusted, transparent, and inclusive alternative, rooted in ethical lending practices and financial empowerment.

Founded in 2017, Snapmint is a profitable, high-growth company, doubling year-on-year. Our founding team, alumni of IIT Bombay and ISB, brings deep expertise from organizations such as Oyo, Ola, and Maruti Suzuki, and has successfully built and exited ventures across ad-tech, patent analytics, and bank-tech. At Snapmint, we are building the future of responsible consumer finance, simple, transparent, and customer-first.

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 (Chi-square, 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").