Collection Analytics and Modeling

Job Overview:

The role involves developing and optimizing scorecards, early warning models, and segmentation frameworks to improve recovery rates and reduce delinquencies. You will collaborate with risk, operations, and product teams to design data-backed strategies, run A/B tests, and track key collection KPIs. Strong skills in SQL, Python, and BI tools are essential, 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 modelling.

• Design segmentation strategies for bucket-wise, geography-wise, and product-wise collections.

• Partner with operations to run champion–challenger strategies and A/B tests to optimize field and digital collections.

• Build dashboards to track collection KPIs, including DPD movement, cure rates, and agency performance.

• Work closely with credit policy and risk to ensure collection strategies align with risk appetite and loss forecasts.

• Collaborate with data engineering and product teams to enable 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 modelling, logistic regression, and machine learning techniques for risk prediction.

• Hands-on with BI tools (Power BI / Tableau / Looker) for visual analytics.

• Understanding of collection operations workflows, dialler 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.