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 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 engineering team to help implement the Risk strategies.
- Work with the Data Science team to effectively provide inputs on the key model.
- Create a deep level understanding of the various data sources.
- Should be able to understand the business problems and help convert them into analytical solutions.
Requirements:
- Bachelor's degree in Computer Science, Engineering or a related field from a top-tier institution.
- 4-9 years of experience working in Data science/Risk Analytics/Credit Risk/Fraud Risk.
- 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.
- Deep understanding of statistical concepts including descriptive analysis.
- 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 SME/LAP/Secured lending experience is preferred.
- Should have a good understanding of various unsecured credit products.