Credit Saison - Associate/Senior Associate - Risk Analytics

Roles & Responsibilities :

- Conduct Portfolio Analysis and Monitor Portfolio delinquencies at a micro level, identification of segments, programs, locations, and profiles which 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 the opportunity to further increase the quality of the portfolio

- 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 optimise 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 convert them into the analytical solutions

Required skills & Qualifications :

- Bachelor's degree in Computer Science, Engineering or related field from top tier (IIT/IIIT/NIT/BITS)

- 2-5 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/ SME/LAP/Secured lending experience is preferred