• Bachelor Degree or above in Economics, Financial Mathematics, Quantitative Finance, Computer Science (Machine Learning), Data Science, Mathematics, Statistics or other quantitative related fields
• 2-3 years progressive experience in credit risk analytics, credit risk modeling, credit scorecard development, data analytics in Financial Services sector (banking, consumer finance, auto finance, fintech, …). Candidate with strong data analytics experience and skills in other industries such as e-commerce, digital marketing, … can be considered if having appropriate mindset and skill
• Good understanding statistical methods and models such as Logistic Regression, Decision Tree, k-mean clustering, hypothesis testing (Wald and Chi-squared test, p-value), parametric estimation (OLS, MLE), central limit theorem, …. Understanding Machine Learning methods is a plus
• Have good experience on DWH/ Data Mart/ Database practices, strong coding/ programing skills for data analytics in SQL or SAS. Ability of coding for data analytics in Python (pandas, numpy, scipy, scikit-learn)/ R is a big plus
• Working with People
• Good presentation skill
• Good written and spoken English