Associate/Senior Associate - Consumer Risk

As an employee at Credit Saison India, you will be part of one of the country's fastest-growing Non-Bank Financial Company (NBFC) lenders. Credit Saison India is committed to evolving its offerings in India for the long-term, serving MSMEs, households, individuals, and more. With a branch network of 45 physical offices, 1.2 million active loans, an AUM of over US$1.5B, and an employee base of about 1,000 people, Credit Saison India is dedicated to creating resilient and innovative financial solutions for positive impact. Key Responsibilities: - 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 Masters degree in Statistics, Economics, Computer Science, or other Engineering disciplines. - 2+ 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 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.,