All fields marked * are required
Job Description: Lead – Collection Scorecard Analytics
Location: Mumbai, BKC Kurla West
Experience: 10+ Years
Work Mode: 5.5 Days WFO(1st, 3rd and 5th Sat working)
Mandatory : Experience in Banking, NBFC, Fintech, or Credit Risk domain
Role Overview
We are seeking an experienced Lead – Collection Analytics to develop, validate, and monitor collection scorecards that optimize debt collection strategies and improve portfolio performance for different asset products of the Bank. The role involves leveraging advanced analytics, machine learning, and statistical modelling techniques to enhance collection efficiency, reduce delinquency, and drive data-driven decision-making.
Key Responsibilities
Scorecard Development
Develop and deploy Collection Scorecards for various customer segments and products.
Build predictive models for delinquency, roll-rate, recovery, and payment propensity.
Perform feature engineering, variable selection, and model tuning.
Evaluate model performance using KS, GINI, ROC-AUC, PSI, and other statistical measures.
Analytics & Insights
Analyze collection portfolio trends, customer behavior, and recovery patterns.
Identify key risk drivers impacting collections and recommend strategy improvements.
Conduct segmentation analysis to optimize collection treatment strategies.
Design challenger models and continuously improve existing scorecards.
Strategy & Business Support
Work closely with Collection, Risk, Product, and Business teams to implement analytics-driven collection strategies.
Recommend customer-level treatment actions based on scorecard outputs.
Support policy formulation and collection workflow optimization.
Measure impact of collection initiatives through A/B testing and performance tracking.
Model Monitoring & Governance
Monitor scorecard performance and stability post-deployment.
Perform periodic validation and recalibration of models.
Ensure compliance with regulatory and model governance requirements.
Prepare model documentation, validation reports, and audit responses.
Leadership
Lead a team of analysts/data scientists.
Mentor team members on statistical modeling and machine learning techniques.
Manage stakeholder communication and project delivery.
Required Skills
Technical Skills
Strong understanding of Collections, Delinquency, Recovery, and Credit Risk Analytics.
Strong expertise in Python, SQL, SAS, or R.
Experience in Logistic Regression, XGBoost, Random Forest, LightGBM, and related ML algorithms.
Hands-on experience with scorecard development methodologies (WOE, IV, Binning).
Experience with model monitoring and validation frameworks.
Data & Tools
Python (Pandas, NumPy, Scikit-learn, XGBoost)
SQL (Athena, Hive, Presto, Oracle, SQL Server, etc.)
Power BI / Tableau
Excel and advanced analytics tools
Preferred Qualifications
Bachelor's/Master's degree in Statistics, Mathematics, Economics, Engineering, Data Science, or related field.
Experience in Banking, NBFC, Fintech, or Credit Risk domain.
Knowledge of RBI regulations, model governance, and risk management frameworks.
Key Performance Indicators (KPIs)
Improvement in collection efficiency and recovery rates.
Reduction in delinquency and roll-forward rates.
Scorecard predictive power (KS, GINI, AUC).
Timely model deployment and monitoring.
Stakeholder satisfaction and business impact.
Nice-to-Have
Exposure to MLOps, SHAP Explainability, Model Monitoring, and Generative AI applications in Collections Analytics.
Required
Preferred
All fields marked * are required
Job Description: Lead – Collection Scorecard Analytics
Location: Mumbai, BKC Kurla West
Experience: 10+ Years
Work Mode: 5.5 Days WFO(1st, 3rd and 5th Sat working)
Mandatory : Experience in Banking, NBFC, Fintech, or Credit Risk domain
Role Overview
We are seeking an experienced Lead – Collection Analytics to develop, validate, and monitor collection scorecards that optimize debt collection strategies and improve portfolio performance for different asset products of the Bank. The role involves leveraging advanced analytics, machine learning, and statistical modelling techniques to enhance collection efficiency, reduce delinquency, and drive data-driven decision-making.
Key Responsibilities
Scorecard Development
Develop and deploy Collection Scorecards for various customer segments and products.
Build predictive models for delinquency, roll-rate, recovery, and payment propensity.
Perform feature engineering, variable selection, and model tuning.
Evaluate model performance using KS, GINI, ROC-AUC, PSI, and other statistical measures.
Analytics & Insights
Analyze collection portfolio trends, customer behavior, and recovery patterns.
Identify key risk drivers impacting collections and recommend strategy improvements.
Conduct segmentation analysis to optimize collection treatment strategies.
Design challenger models and continuously improve existing scorecards.
Strategy & Business Support
Work closely with Collection, Risk, Product, and Business teams to implement analytics-driven collection strategies.
Recommend customer-level treatment actions based on scorecard outputs.
Support policy formulation and collection workflow optimization.
Measure impact of collection initiatives through A/B testing and performance tracking.
Model Monitoring & Governance
Monitor scorecard performance and stability post-deployment.
Perform periodic validation and recalibration of models.
Ensure compliance with regulatory and model governance requirements.
Prepare model documentation, validation reports, and audit responses.
Leadership
Lead a team of analysts/data scientists.
Mentor team members on statistical modeling and machine learning techniques.
Manage stakeholder communication and project delivery.
Required Skills
Technical Skills
Strong understanding of Collections, Delinquency, Recovery, and Credit Risk Analytics.
Strong expertise in Python, SQL, SAS, or R.
Experience in Logistic Regression, XGBoost, Random Forest, LightGBM, and related ML algorithms.
Hands-on experience with scorecard development methodologies (WOE, IV, Binning).
Experience with model monitoring and validation frameworks.
Data & Tools
Python (Pandas, NumPy, Scikit-learn, XGBoost)
SQL (Athena, Hive, Presto, Oracle, SQL Server, etc.)
Power BI / Tableau
Excel and advanced analytics tools
Preferred Qualifications
Bachelor's/Master's degree in Statistics, Mathematics, Economics, Engineering, Data Science, or related field.
Experience in Banking, NBFC, Fintech, or Credit Risk domain.
Knowledge of RBI regulations, model governance, and risk management frameworks.
Key Performance Indicators (KPIs)
Improvement in collection efficiency and recovery rates.
Reduction in delinquency and roll-forward rates.
Scorecard predictive power (KS, GINI, AUC).
Timely model deployment and monitoring.
Stakeholder satisfaction and business impact.
Nice-to-Have
Exposure to MLOps, SHAP Explainability, Model Monitoring, and Generative AI applications in Collections Analytics.
Required
Preferred