Role Overview
We are seeking a highly analytical and data-driven Collection Strategy Analyst with deep expertise in risk management and end-to-end collections lifecycle strategy. The ideal candidate will possess strong capabilities in portfolio analytics, risk segmentation, AI/ML modeling, and large-scale data interpretation to design and optimize collection strategies across the entire lifecycle — from pre-due to recovery.
This role requires a strong academic background from premier institutes (IIT, IIM, or equivalent top-tier institutions) with a Master’s degree in IT, Data Science, Statistics, or related quantitative disciplines.
Key Responsibilities
1. End-to-End Collection Strategy Design
- Develop and optimize data-driven collection strategies across:
- Pre-due stage
- Early bucket collections
- Late bucket collections
- Recovery and write-off management
- Design stage-wise treatment strategies to maximize resolution rates and minimize roll-forward.
2. Risk & Portfolio Analytics
- Conduct in-depth portfolio analysis to identify risk trends, delinquency drivers, and behavioral patterns.
- Develop and monitor portfolio scorecards.
- Perform risk segmentation to enable differentiated strategy deployment.
- Analyze large historical datasets to extract actionable insights and improve collection efficiency.
3. Advanced Data & AI/ML Implementation
- Develop and deploy AI/ML models for:
- Delinquency prediction
- Roll rate forecasting
- Recovery propensity
- Contactability optimization
- Partner with data engineering teams for seamless data integration and model deployment.
- Evaluate model performance, stability, and recalibration needs.
4. Strategy Performance Monitoring
- Design performance dashboards and KPIs for collection effectiveness.
- Monitor employee scorecards aligned with productivity, efficiency, and compliance.
- Conduct champion-challenger testing to continuously optimize strategies.
5. Big Data & Data Interpretation
- Work with large, complex structured and unstructured datasets.
- Perform cohort analysis, roll-rate analysis, vintage analysis, and behavioral segmentation.
- Interpret historical performance data to refine future strategy design.
6. Stakeholder Collaboration
- Collaborate with Risk, Operations, Product, Technology, and Senior Leadership teams.
- Present analytical findings and strategic recommendations to leadership.
Required Qualifications
- Master’s or Bachelor degree in Engineering, Computer Science, IT, Data Science, Statistics, Mathematics, Economics, or related quantitative discipline.
- Preferred education from premier institutions such as IIT, IIM, ISI, or other top-tier institutes.
- 4–8+ years of relevant experience in collections analytics, credit risk, or financial services.
Technical Skills
- Strong proficiency in:
- SQL
- Python / R
- Advanced Excel
- Data visualization tools (Power BI / Tableau)
- Deep understanding of:
- Portfolio scorecards
- Risk segmentation methodologies
- Employee performance scorecards
- Roll rate and vintage analysis
- AI/ML model development and validation
- Big data ecosystems and data integration frameworks
Core Competencies
- Strong analytical and problem-solving skills
- Strategic thinking with business acumen
- Deep understanding of credit risk and collections lifecycle
- Ability to interpret complex datasets and convert insights into executable strategies
- Excellent communication and stakeholder management skills
Preferred Attributes
- Experience in NBFC/Banking/FinTech collections environment
- Exposure to regulatory compliance in collections
- Hands-on experience with large-scale portfolio management
- Proven track record of driving measurable improvement in collection efficiency and recovery rates
