Valiance Solutions - Data Scientist - Digital Credit Risk Models

Description : Job Title : Data Scientist Credit Risk Analyst (Fintech) Location : Delhi Experience : 1 to 5 Years Employment Type : Full-Time Education : B.Tech from Tier 1 Colleges preferred About the Role : We are looking for a hands-on Data Scientist Credit Risk to build and optimize risk models for digital lending products. In this role, you will work on real-time underwriting models, alternate data-based risk assessment, and portfolio risk monitoring in a fast-paced fintech environment. You will collaborate closely with product, risk, and engineering teams to design robust credit models that enable scalable and responsible lending. Key Responsibilities : Digital Credit Risk Model Development : - Build and deploy Application Scorecards, Behavioral Scorecards, and PD models for unsecured lending products. - Define and construct target variables (DPD-based default definitions such as 30+, 60+, 90+ DPD). - Design observation and performance windows aligned with business objectives. - Develop logistic regression and ML-based models for underwriting and line assignment. - Implement WOE transformation, IV analysis, feature engineering, and segmentation strategies. Model Validation & Governance : - Perform end-to-end model validation, including : - Out-of-Time (OOT) validation - Cross-validation - Sensitivity and stability analysis Evaluate models using : - KS, AUC-ROC, Gini - PSI (Population Stability Index) - Lift & Gain charts - Conduct back-testing and monitor model drift in live portfolios. - Prepare validation documentation and support audit/compliance requirements. Real-time Risk & Portfolio Monitoring - Work with engineering teams to integrate models into underwriting pipelines. - Monitor portfolio performance, delinquency trends, and risk segmentation. - Recommend cut-off optimization and risk-based pricing strategies. - Identify early warning signals and improve collections prioritization models. Alternate Data & Advanced Analytics - Leverage fintech-relevant data sources such as : a. Bureau data b. Transactional data c. Bank statement analysis d. Digital footprint/alternate data - Experiment with advanced ML models while ensuring explainability Technical Skills : - Strong proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels) - Experience in : a. Credit Risk Modeling in fintech/NBFC b. PD modeling & scorecard development c. Target variable construction d. OOT validation & performance monitoring - Strong understanding of : a. Logistic Regression b. WOE/IV c. KS, Gini, AUC d. PSI & Model Stability - Good SQL skills for large-scale data handling. Domain Knowledge : Understanding of : - Digital lending lifecycle - Underwriting frameworks - DPD-based default definitions - Risk-based pricing - Portfolio risk management Description : Job Title : Data Scientist Credit Risk Analyst (Fintech) Location : Delhi Experience : 1 to 5 Years Employment Type : Full-Time Education : B.Tech from Tier 1 Colleges preferred About the Role : We are looking for a hands-on Data Scientist Credit Risk to build and optimize risk models for digital lending products. In this role, you will work on real-time underwriting models, alternate data-based risk assessment, and portfolio risk monitoring in a fast-paced fintech environment. You will collaborate closely with product, risk, and engineering teams to design robust credit models that enable scalable and responsible lending. Key Responsibilities : Digital Credit Risk Model Development : - Build and deploy Application Scorecards, Behavioral Scorecards, and PD models for unsecured lending products. - Define and construct target variables (DPD-based default definitions such as 30+, 60+, 90+ DPD). - Design observation and performance windows aligned with business objectives. - Develop logistic regression and ML-based models for underwriting and line assignment. - Implement WOE transformation, IV analysis, feature engineering, and segmentation strategies. Model Validation & Governance : - Perform end-to-end model validation, including : - Out-of-Time (OOT) validation - Cross-validation - Sensitivity and stability analysis Evaluate models using : - KS, AUC-ROC, Gini - PSI (Population Stability Index) - Lift & Gain charts - Conduct back-testing and monitor model drift in live portfolios. - Prepare validation documentation and support audit/compliance requirements. Real-time Risk & Portfolio Monitoring - Work with engineering teams to integrate models into underwriting pipelines. - Monitor portfolio performance, delinquency trends, and risk segmentation. - Recommend cut-off optimization and risk-based pricing strategies. - Identify early warning signals and improve collections prioritization models. Alternate Data & Advanced Analytics - Leverage fintech-relevant data sources such as : a. Bureau data b. Transactional data c. Bank statement analysis d. Digital footpr