Roles & Responsibilities:

- Conduct Portfolio Analysis and Monitor Portfolio delinquencies at a micro level, identification of segments, programs, locations, and profiles that are delinquent or working well.

- Design and implement risk strategies across the customer lifecycle (acquisitions, portfolio management, fraud, collections, etc.)

- Identify trends by performing necessary analytics at various cuts for the Portfolio

- Provide analytical support to various internal reviews of the portfolio and help identify the opportunity to further increase the quality of the portfolio

- Use data-driven insights to improve risk selection, pricing, and capital allocation.

- Work with the Product team and the engineering team to help implement the Risk strategies

- Work with the Data Science team to effectively provide inputs on the key model variables and optimise the cut-off for various risk models

- Create a deep-level understanding of the various data sources (Traditional as well as alternative) and optimum use of the same in underwriting

- Should have a good understanding of various unsecured credit products

- Should be able to understand the business problems and help solve them using analytical methods

- Lead a high-performing credit risk team

- Identify emerging risks, concentration issues, and early warning signals

- Enhance automation, digital underwriting, and advanced analytics in credit risk processes.

- Improve turnaround times, data quality, and operational efficiency without compromising risk standards

Required skills & Qualifications:

- Strong expertise in credit risk management, underwriting strategies, and portfolio analytics

- Excellent stakeholder management and communication abilities

- Strategic mindset with the ability to balance risk and growth

- Advanced analytical and problem-solving skills

- Bachelor's degree in Computer Science, Engineering, or related field from a top-tier (IIT/IIIT/NIT/BITS)

- 6+ years of experience working in Data Science/Risk Analytics/Risk Management, with experience in building models/Risk strategies or generating risk insights

- Proficiency in SQL and other analytical tools/scripting languages such as Python or R

- Deep understanding of statistical concepts, including descriptive analysis, experimental design and measurement, Bayesian statistics, confidence intervals, Probability distributions

- Proficiency with statistical and data mining techniques

- Proficiency with machine learning techniques such as decision tree learning, etc.

- Should have experience working with both structured and unstructured data

- Fintech or Retail consumer digital lending experience is preferred