Develop predictive and decision models to be deployed in systems. This includes the analysis of historical data, data cleaning, sampling, variable selection, modeling and performance evaluation.
Conduct regular validation and monitoring on the performance of decision models for retail portfolios.
Formulate data driven decision strategies across the customer credit lifecycle including origination, portfolio management and collections.
Streamline the process and workflow across the customer credit lifecycle.
Prepare the user requirement documents and participate in user acceptance tests on decision models.
Requirements:
Bachelor degree holder or above in Mathematics, Statistics, Risk Management, Computer Science, Engineering or Operations Research
Minimum 3 years' working experience in banking, financial institution or credit risk modeling
At least 1 year working experience in Python, R or SAS, and familiarity with these software program coding standards and best practices
Strong statistical knowledge of the following: Segmentation, Linear and Logistic Regressions, Machine Learning algorithms such as Random Forest and XGBoost
Good communication skill, passionate and energetic
Good command of written and spoken Chinese and English. Fluent Mandarin will be an advantage