Experience : 4-10 yrs
About the Role:
We are looking for a highly analytical and business-driven Senior Data Scientist to lead data science initiatives in credit risk and collections strategy for our home loan portfolio. The role involves building predictive models, optimizing credit decisions, and enhancing collections efficiency through data-driven insights.
We are looking for an immediate joiner or with notice of 1 month.
Key Responsibilities:
1. Credit Risk Modeling
Develop and deploy predictive models for:
- Credit scoring (application & behavioral)
- Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD)
- Analyze borrower behavior and creditworthiness using structured and alternative data.
- Monitor model performance and ensure stability over time.
2. Collections Strategy & Optimization
- Design data-driven collection strategies across delinquency buckets
Build models for:
- Roll rate prediction
- Recovery optimization
- Customer segmentation for collections prioritization
- Recommend treatment strategies (call, legal, settlement, restructuring)
3. Data Analysis & Insights
- Perform deep-dive analysis on portfolio performance, delinquencies, and losses
- Identify early warning signals and risk triggers
- Translate complex findings into actionable business recommendations
4. Experimentation & Strategy Testing
- Design and evaluate A/B tests for risk policies and collections interventions
- Measure impact of strategy changes on recovery rates and NPA reduction
5. Collaboration
- Work closely with Risk, Underwriting, and Collections teams
- Partner with Product and Business stakeholders
- Coordinate with Data Engineering teams for pipeline development
Required Skills & Qualifications:
- Bachelor's/Master's degree in Data Science, Statistics, Mathematics, Engineering, or related field
- 3-5 years of experience in data science, preferably in banking, NBFC, or fintech (lending domain)
Strong understanding of:
- Credit risk modeling
- Collections analytics
- Lending lifecycle
Technical Skills
- Programming: Python,SQL
Machine Learning techniques:
- Logistic Regression
- Decision Trees
- Random Forests
- Gradient Boosting
- Experience with model deployment and monitoring
Analytical Skills:
- Strong statistical knowledge and hypothesis testing
- Experience working with large datasets and feature engineering
- Ability to interpret model outputs for business stakeholders
Good to Have:
- Experience in home loans / secured lending
- Knowledge of regulatory frameworks (RBI guidelines, Basel norms)
- Exposure to alternative data and bureau data
- Experience with tools DataBricks, or cloud platforms (AWS/GCP/Azure)
Key Competencies:
- Strong problem-solving mindset with business acumen
- Ability to work in a fast-paced, cross-functional environment
- Excellent communication and stakeholder management skills
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