About the role
Responsibilities:
- Conduct portfolio analysis and monitor portfolio delinquencies at a micro level, identifying segments, programs, locations, and profiles that are delinquent or performing well.
- Help develop credit strategies across the customer lifecycle (acquisitions, 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 opportunities to further increase the quality of the portfolio.
- Work with the product team and 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 cutoff 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.
Requirements:
- Should have a good understanding of various unsecured credit products.
- Should be able to understand the business problems and help convert them into analytical solutions.
- 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, and 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/SME/LAP/secured lending experience is preferred.