We are seeking a data-driven professional with deep expertise in collections analytics to drive strategy, performance monitoring, and modeling for our lending portfolio. The ideal candidate will combine strong analytical skills with practical experience in collections strategy, scorecard development, and cross-functional collaboration.

Key Responsibilities

Scorecard & Model Development: Design and enhance collection scorecards and early warning models to predict delinquencies and support proactive risk mitigation.
Performance Analysis: Conduct vintage analysis, roll-rate and flow-rate tracking, and lag-based recovery modeling to understand portfolio dynamics.
Segmentation & Strategy: Create advanced segmentation strategies (bucket-wise, geography-wise, and product-wise) to tailor collection approaches and optimize recovery.
Strategy Experimentation: Collaborate with operations to run A/B tests and championchallenger strategies across digital and field collections.
KPI Tracking & Dashboards: Build and maintain dashboards to track key collection metrics such as DPD movement, cure rates, recovery curves, and agency productivity.
Cross-functional Collaboration: Work closely with credit risk and policy teams to align collection strategies with risk appetite and loss expectations.
Technology Enablement: Partner with data engineering and product teams to build real-time analytics for portfolio monitoring and collector performance insights.

Required Skills

Proficiency in SQL, Python, and Excel for data analysis, automation, and modeling.
Hands-on experience in statistical modeling, including logistic regression and basic machine learning techniques for predictive analytics.
Expertise in BI and visualization tools (Power BI, Tableau, Looker) for performance reporting and insights.
Deep understanding of collections workflows, including dialer management, digital strategies, and agency/vendor performance tracking.
Strong ability to translate data insights into actionable business recommendations and strategy enhancements.

Preferred Qualifications

410 years of experience in collections or credit risk analytics in a consumer lending or fintech environment.
Exposure to digital collections, tele-calling optimization, or field collections productivity models.
Educational background in Statistics, Mathematics, Economics, Engineering, or a related quantitative discipline.

About Kissht:

Kissht, a Great Place to Work certified organization, is a consumer-first credit app that is transforming the landscape of consumer credit. As one of the fastest-growing and most respected FinTech companies, Kissht is a pioneer in data and machine-based lending. With over 15 million customers, including 40% from tier 2 cities and beyond, we offer both short and long-term loans for personal consumption, business needs, and recurring expenses.

Founded by Ranvir and Krishnan, alumni of IIT and IIM, and backed by renowned investors like Endiya Partners, the Brunei Investment Authority, and the Singapore Government, Kissht is synonymous with excellence in the industry. Join us and be a part of a dynamic, innovative company that is changing the future of financial technology.

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