Description :

- Build and enhance models for credit risk, underwriting, customer eligibility, risk-based pricing, credit limit assignment, delinquency prediction, and portfolio monitoring.

- Develop models for Probability of Default, bureau score enhancement, early warning signals, collections prioritization, fraud risk, loss forecasting, and customer affordability assessment.

- Work with bureau data from sources such as CIBIL, Experian, Equifax, CRIF, or similar credit bureaus.

- Engineer bureau-based features such as credit utilization, enquiry patterns, repayment behavior, credit vintage, open and closed tradelines, delinquency history, unsecured exposure, EMI burden, settlements, write-offs, and defaults.

- Support financial risk modelling including vintage analysis, cohort analysis, roll-rate analysis, expected loss estimation, portfolio risk, and stress testing.

Required Skills & Qualifications :

- 6 - 7 years of experience in data science, preferably across fintech lending, digital lending, NBFC, banking, credit analytics, AdTech, growth analytics, performance marketing, or consumer internet.

- Strong hands-on experience in bureau data, credit risk modelling, propensity modelling, affinity modelling, customer segmentation, campaign modelling, and ML-based decisioning.

- Good understanding of lending concepts such as underwriting, eligibility, approval, disbursal, delinquency, collections, portfolio risk, customer affordability, and repayment behavior.

- Ability to work with alternate data sources such as behavioral data, clickstream data, app/web events, bank statements, transaction data, campaign data, CRM data, device signals, and third-party data.

- Strong knowledge of ML and DL algorithms including classification, regression, clustering, recommendation systems, ranking, uplift modelling, time-series forecasting, and anomaly detection.

- Strong programming skills in Python and SQL.

- Experience with libraries such as pandas, NumPy, scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, or PyTorch.

- Strong understanding of business metrics such as approval rate, conversion rate, default rate, delinquency rate, repayment rate, CAC, CPA, ROAS, LTV, CTR, CVR, and risk-adjusted profitability.

- Ability to translate ambiguous business problems into analytical frameworks and scalable modelling solutions.

- Strong stakeholder management and communication skills.