- Workplace
- Onsite
- Type
- Internship
- Department
- IT
- Seniority
- C-Level
- Education
- Bachelor
- Source
- Freshteam
Description
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)
8+ 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
