Responsible for independent development or validation of risk scoring models, including antifraud, credit underwriting or collection models and challenging current approaches to risk management and modeling;
Construct dataset used for development or validation, ensure the sample is representative of production population and data is in good quality;
Creative feature engineering skills;
Develop high-quality machine learning models;
Perform quantitative assessment of model performance based on development, validation and out-of-time samples;
Documentation of model development or validation and communication of
results to senior manager and presentation to relevant departments;
Develop in-depth knowledge and expertise in risk data science, continuously improve model performance, research the latest modeling practices.
REQUIREMENTS :
BS / MS degree in data science, statistics, computer science, applied mathematics or any quantitative discipline;
Minimum 2 years experience in data science, developing statistical models in banking, financial industry or ecommerce;
Experience with SQL;
Experience with Python data science stack (such as numpy, pandas, scikit learn, xgboost, etc.);
Practical experience with machine learning;
Good English for business.
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