Kissht - Collection Analytics & Modeling Role

Description:
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 championchallenger 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.