Credit Saison - Associate/Senior Associate - Data Science - Risk Modelling

Basic Qualifications

- 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 distributions

- 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 reduction algorithms

- Proficiency with machine learning techniques such as clustering, decision tree learning, naive Bayes

- 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

Key Responsibilities :

- 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.