Senior Data Scientist - Credit Risk & Collections Strategy - Home Loan Portfolio

As a Senior Data Scientist specializing in credit risk and collections strategy for home loan portfolios, your role will involve building predictive models, optimizing credit decisions, and enhancing collections efficiency through data-driven insights. Key Responsibilities: - Develop and deploy predictive models for credit scoring (application & behavioral), Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). - Analyze borrower behavior and creditworthiness using structured and alternative data. - Monitor model performance and ensure stability over time. - Design data-driven collection strategies across delinquency buckets. - Build models for roll rate prediction, recovery optimization, and customer segmentation for collections prioritization. - Recommend treatment strategies such as call, legal, settlement, and restructuring. - Perform deep-dive analysis on portfolio performance, delinquencies, and losses. - Identify early warning signals and risk triggers. - Translate complex findings into actionable business recommendations. - Design and evaluate A/B tests for risk policies and collections interventions. - Measure the impact of strategy changes on recovery rates and NPA reduction. - Work closely with Risk, Underwriting, and Collections teams. - Partner with Product and Business stakeholders. - Coordinate with Data Engineering teams for pipeline development. Required Skills & Qualifications: - Bachelor's/Master's degree in Data Science, Statistics, Mathematics, Engineering, or related field. - 3-5 years of experience in data science, preferably in banking, NBFC, or fintech (lending domain). - Strong understanding of credit risk modeling, collections analytics, and lending lifecycle. Technical Skills: - Programming: Python, SQL. - Machine Learning techniques: Logistic Regression, Decision Trees, Random Forests, Gradient Boosting. - Experience with model deployment and monitoring. Analytical Skills: - Strong statistical knowledge and hypothesis testing. - Experience working with large datasets and feature engineering. - Ability to interpret model outputs for business stakeholders. Good to Have: - Experience in home loans / secured lending. - Knowledge of regulatory frameworks (RBI guidelines, Basel norms). - Exposure to alternative data and bureau data. - Experience with tools like DataBricks, or cloud platforms (AWS/GCP/Azure). Key Competencies: - Strong problem-solving mindset with business acumen. - Ability to work in a fast-paced, cross-functional environment. - Excellent communication and stakeholder management skills. As a Senior Data Scientist specializing in credit risk and collections strategy for home loan portfolios, your role will involve building predictive models, optimizing credit decisions, and enhancing collections efficiency through data-driven insights. Key Responsibilities: - Develop and deploy predictive models for credit scoring (application & behavioral), Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). - Analyze borrower behavior and creditworthiness using structured and alternative data. - Monitor model performance and ensure stability over time. - Design data-driven collection strategies across delinquency buckets. - Build models for roll rate prediction, recovery optimization, and customer segmentation for collections prioritization. - Recommend treatment strategies such as call, legal, settlement, and restructuring. - Perform deep-dive analysis on portfolio performance, delinquencies, and losses. - Identify early warning signals and risk triggers. - Translate complex findings into actionable business recommendations. - Design and evaluate A/B tests for risk policies and collections interventions. - Measure the impact of strategy changes on recovery rates and NPA reduction. - Work closely with Risk, Underwriting, and Collections teams. - Partner with Product and Business stakeholders. - Coordinate with Data Engineering teams for pipeline development. Required Skills & Qualifications: - Bachelor's/Master's degree in Data Science, Statistics, Mathematics, Engineering, or related field. - 3-5 years of experience in data science, preferably in banking, NBFC, or fintech (lending domain). - Strong understanding of credit risk modeling, collections analytics, and lending lifecycle. Technical Skills: - Programming: Python, SQL. - Machine Learning techniques: Logistic Regression, Decision Trees, Random Forests, Gradient Boosting. - Experience with model deployment and monitoring. Analytical Skills: - Strong statistical knowledge and hypothesis testing. - Experience working with large datasets and feature engineering. - Ability to interpret model outputs for business stakeholders. Good to Have: - Experience in home loans / secured lending. - Knowledge of regulatory frameworks (RBI guidelines, Basel norms). - Exposure to alternative data and bureau data. - Experience with tools like DataBricks, or cloud platfor