About Credit Saison:
Credit Saison is a Japanese MNC that has setup a lending business here in India. Through its NBFC here in India, that is primarily focused on unsecured lending to Consumer and SME, Credit Saison is looking to replicate its success in Japan in the Indian Market.
The India team consists of leaders from India’s biggest fintech success stories over the past 5 years. Credit Saison (India) aims to create the next wave in the fintech in India. We will build a world class, cutting-edge technology platforms for the global vision and expansion of the company. This will be a high-performance enabled work culture, career focused and with the aim of changing/aiding the growth of this sector.
We are on to something big, come be part of this journey.
● Masters or Bachelors degree in, Statistics, Economics, Machine Learning, Operations
Research, Computer Science or other quantitative fields. (If M.S. degree, a minimum of
1+ years of industry experience required and if Bachelor's degree, a minimum of 2+ years
of industry experience required)
● 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
● Should have an understanding of defining and Testing of Hypothesis, statistical measure
of central tendency, Population and sample, sampling Techniques, Correlation and its
measures and CL theorem
● Proficiency with statistical and data mining techniques including generalized linear
model/regression, logistic regression, random forest, boosting, trees,dimensionality
● Proficiency with machine learning techniques such as clustering, decision tree learning,
● Able to deal well with uncertainty and unstructured problems to be solved
● Should have an experience working with structured and unstructured data with applied
Data science/Machine learning techniques
● Define experiments based on the model to understand and thus enhance the model
● Experience in retail lending is a plus
● Develop end to end Credit Risk scorecards ranging from applications to behaviour to
collections scorecard using techniques such as linear model/regression, logistic
regression, random forest, boosting/bagging trees, dimensionality reduction algorithms
● Optimize models’ outcomes to help business and drive growth
● Analyze, interpret and present outcome/results to stakeholders; Set up model monitoring
and understand the reasons of model expectations vs actual outcome
● Own and deliver multiple and complex analytic projects. This would require an
understanding of business context, conversion of business problems in modeling, and
implementing such solutions to create business value.