Senior Associate - Data Risk Management

Description :

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)

- 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.