Collection Analytics and Modeling

As a Data Analyst specialized in collections and risk analytics, your role will involve developing and optimizing scorecards, early warning models, and segmentation frameworks to enhance recovery rates and reduce delinquencies. You will collaborate with risk, operations, and product teams to design data-backed strategies, conduct A/B tests, and monitor key collection KPIs. Your proficiency in SQL, Python, and BI tools is crucial, along with a solid understanding of collection workflows and analytics-driven decision-making. Key Responsibilities: - Develop, monitor, and enhance collection scorecards and early warning models to predict delinquency and optimize recovery efforts. - Perform vintage and roll-rate analyses, flow rate tracking, and lag-based recovery modeling. - Design segmentation strategies for bucket-wise, geography-wise, and product-wise collections. - Partner with operations to implement champion-challenger strategies and A/B tests for field and digital collections optimization. - Create dashboards to monitor collection KPIs such as DPD movement, cure rates, and agency performance. - Collaborate with credit policy and risk teams to ensure collection strategies align with risk appetite and loss forecasts. - Work with data engineering and product teams to provide real-time portfolio insights and collector performance analytics. Required Skills: - Strong proficiency in SQL, Python, and Excel for data analysis and automation. - Experience with statistical modeling, logistic regression, and machine learning techniques for risk prediction. - Hands-on experience with BI tools (Power BI / Tableau / Looker) for visual analytics. - Understanding of collection operations workflows, dialer strategies, and agency management KPIs. - Ability to translate insights into actionable business recommendations. Preferred Qualifications: - 4-10 years of experience in collections or risk analytics within consumer lending. - Exposure to digital collection platforms, tele-calling optimization, or field force productivity models. - Educational background in Statistics, Mathematics, Economics, Engineering, or related field.,