- Conduct Portfolio Analysis and Monitor Portfolio delinquencies at a micro level, identification of segments, programs, locations, and profiles which 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 Product team and engineering team to help implements the Risk strategies
- Work with 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 alternate) and optimum use of the same in underwriting
- Should have good understanding about various unsecured credit products
- Should be able to understand the business problems and helps solving them by the analytical methods
- Lead 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 top tier (IIT/IIIT/NIT/BITS)
- 7+ years of experience working in Data science/Risk Analytics/Risk Management with experience in building the 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 an experience working with both structured and unstructured data
- Fintech or Retail consumer digital lending experience is preferred