Overview
About the Organization
A fast-growing, technology-driven NBFC lender, focused on wholesale lending, direct lending, and tech-enabled partnerships with NBFCs and fintech's. The organization leverages advanced underwriting and analytics to enable scalable lending, addressing Indias significant credit gapparticularly among underserved and underpenetrated segments.
With a strong presence across India, the company manages millions of active loans, a multi-billion-dollar AUM, and is backed by a globally reputed financial services group with decades of experience across lending, payments, leasing, and investments. The organization is regulated by the RBI and holds top-tier credit ratings.
Roles & Responsibilities :
- Conduct detailed portfolio analysis and monitor delinquencies at a granular level
- Identify underperforming and high-performing segments, programs, geographies, and customer profiles
- Support development of credit strategies across the customer lifecycle (acquisition, account management, fraud, and collections)
- Perform trend analysis using multiple portfolio cuts and risk dimensions
- Provide analytical insights for internal portfolio reviews and identify opportunities to improve
portfolio quality
- Collaborate with Product and Engineering teams to implement risk strategies
- Partner with the Data Science team to provide inputs on key model variables and optimize
cut-offs for risk models
- Develop deep understanding of traditional and alternative data sources and their application
in underwriting
- Understand unsecured credit products and associated risk dynamics
- Translate business problems into data-driven analytical solutions
Required Skills & Qualifications :
- Bachelors degree in Computer Science, Engineering, or a related field from premier institutes (IIT / IIIT / NIT / BITS preferred)
- 2-5 years of experience in Data Science, Risk Analytics, or Risk Management
- Hands-on experience in building risk models, strategies, or generating risk insights
- Strong proficiency in SQL and scripting/analytics tools such as Python or R
Solid understanding of statistical concepts, including :
- Descriptive analysis
- Experimental design and measurement
- Bayesian statistics
- Confidence intervals and probability distributions
- Experience with statistical and data mining techniques
- Familiarity with machine learning techniques (e.g., decision trees)
- Experience working with structured and unstructured data
- Prior exposure to Fintech, Retail Lending, SME, LAP, or Secured Lending is a plus.