Collaborate and coordinate across functions to dissect business, product, growth, acquisition, retention, and operational metrics.
Execute deep & complex quantitative analyses that translate data into actionable insights
Should have a good understanding of various unsecured credit products
The ability to clearly and effectively articulate and communicate the results of complex analyses
Create a deep-level understanding of the various data sources (Traditional as well as alternative) and optimum use of the same in underwriting.
Work with the Data Science team to effectively provide inputs on the key model variables and optimize the cut-off for various risk models
Helps to develop credit strategies/monitoring framework across the customer lifecycle (acquisitions, management, fraud, collections, etc.)
Conduct Portfolio Analysis and Monitor Portfolio delinquencies at a micro level, identification of segments, programs, locations, and profiles that are delinquent or working well.
Basic Qualifications
Bachelors or Master's degree in, Statistics, Economics, Computer Science, or other Engineering disciplines.
Proficiency in SQL and other analytical tools/scripting languages such as Python or R is a must
Deep understanding of statistical concepts including descriptive analysis, experimental design and measurement, Bayesian statistics, confidence intervals, Probability distributions
Experience and knowledge of statistical modeling techniques: GLM multiple regression, logistic regression, log-linear regression, variable selection, etc.
Good To Have
Exposure to the Fintech industry, preferably digital lending background
Exposure to visualization techniques & tools B.E. (Bachelor of Engineering)
