Snapmint - Credit Risk Analyst

Role Overview :

The Credit Risk Analytics team 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 analyst roles that focus primarily on model building, this position emphasizes portfolio observability, cohort-based performance tracking, and real-time decision optimization across both new and repeat user segments.

You will own critical metrics used by product, risk, and business teams and have the autonomy to challenge policy assumptions using data.

Key Responsibilities :

- Monitor and analyze credit portfolio performance across customer cohorts and risk segments

- Translate large-scale transactional and customer data into actionable insights for approval and risk optimization

- Track real-time decisioning performance for new and repeat customer journeys

- Partner with Product, Risk, and Business teams to influence credit policies and approval strategies

- Build and maintain dashboards and metrics for portfolio observability and performance monitoring

- Perform deep-dive analyses to identify risk trends, anomalies, and profitability drivers

- Support experimentation and policy testing using data-backed insights

Requirements :

Experience :

- 1 - 3 years in credit risk analytics, lending analytics, or consumer fintech metrics (e.g., risk, fraud, chargebacks)

Fintech Exposure :

- Familiarity with instant approval decisioning, high-velocity transaction flows, chargeback/fraud dynamics, and customer acquisition models

Credit & Risk Knowledge :

- Understanding of bureau scores, credit risk fundamentals, and lending KPIs

Data & Analytics Skills :

- Write complex nested queries, window functions, and multi-stage aggregations

- Optimize queries for performance on large-scale (billion-row) datasets

- Strong experience with Pandas, NumPy, and SciPy

- Perform cohort analysis and statistical testing (Chi-square, t-test, survival analysis)

- Build simple predictive models (e.g., logistic regression)

Systems & Tooling :

- Experience working with real-time or near real-time data pipelines

- Exposure to dashboarding tools (Tableau, Power BI, Looker, etc.)

- Familiarity with A/B testing and experimentation frameworks