Responsibilities :

- Conduct Portfolio Analysis and Monitor Portfolio delinquencies at a micro level, identifying 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 optimize 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.

Requirements :

- 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 a related field from a top-tier institution (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.