Senior Data Scientist

As a highly analytical and business-driven Senior Data Scientist, your role will involve leading data science initiatives in credit risk and collections strategy for the home loan portfolio. You will be responsible for building predictive models, optimizing credit decisions, and enhancing collections efficiency through data-driven insights. Key Responsibilities: - Credit Risk Modeling: - 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. - Collections Strategy & Optimization: - 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. - Data Analysis & Insights: - Perform deep-dive analysis on portfolio performance, delinquencies, and losses. - Identify early warning signals and risk triggers. - Translate complex findings into actionable business recommendations. - Experimentation & Strategy Testing: - Design and evaluate A/B tests for risk policies and collections interventions. - Measure the impact of strategy changes on recovery rates and NPA reduction. - Collaboration: - Work closely with Risk, Underwriting, and Collections teams. - Partner with Product and Business stakeholders. - Coordinate with Data Engineering teams for pipeline development. Required 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). 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 highly analytical and business-driven Senior Data Scientist, your role will involve leading data science initiatives in credit risk and collections strategy for the home loan portfolio. You will be responsible for building predictive models, optimizing credit decisions, and enhancing collections efficiency through data-driven insights. Key Responsibilities: - Credit Risk Modeling: - 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. - Collections Strategy & Optimization: - 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. - Data Analysis & Insights: - Perform deep-dive analysis on portfolio performance, delinquencies, and losses. - Identify early warning signals and risk triggers. - Translate complex findings into actionable business recommendations. - Experimentation & Strategy Testing: - Design and evaluate A/B tests for risk policies and collections interventions. - Measure the impact of strategy changes on recovery rates and NPA reduction. - Collaboration: - Work closely with Risk, Underwriting, and Collections teams. - Partner with Product and Business stakeholders. - Coordinate with Data Engineering teams for pipeline development. Required 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). 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