The Credit Risk Analytics owns the analytical foundation of the credit portfolio, translating raw transaction and customer data into actionable insights that drive approval optimisation, 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 emphasises portfolio observability, cohort-based performance tracking, and real-time decisioning optimisation 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.
Requirements
- Fintech: Familiarity with instant approval decisioning, high-velocity transaction flows, chargeback/fraud dynamics, and customer acquisition models.
- 1-3 years in credit risk analytics, lending analytics, or consumer fintech metrics (e. g., fraud, chargeback, risk).
- Credit and Risk Knowledge: Understanding of bureau scoring.
Technical Skills
- SQL Advanced: Write complex nested queries, window functions, and multi-stage aggregations. Optimise 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).
This job was posted by Parvinder Kaur from Snapmint.
