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.
- Helps to 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 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.
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 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/ SME/LAP/Secured lending experience is preferred.
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