Description

• Perform in-depth data cleaning, feature engineering, and analysis of customer application data, behavioral data, and transaction data.
• Develop, validate, and monitor credit risk models and customer behavior prediction models.
• Design data-driven credit risk strategies (e.g., approval, credit limit and collections strategies) aligned with business objectives and risk appetite.
• Prepare comprehensive model documentation, presentations, and business requirement specifications to ensure compliance with regulatory standards.

• Bachelor's degree or higher in Statistics, Computer Science, Information Systems, Applied Mathematics, or a related quantitative field.
• 2+ years of experience in risk analytics or modelling, preferably in retail banking. Fresh graduates with strong academic credentials may also be considered for junior roles.
• Proven experience in formulating and implementing credit risk strategies.
• Proficiency in Python and SAS, with hands-on experience in machine learning frameworks such as Random Forest and XGBoost.
• Prior experience with Large Language Models (LLM) or generative AI applications is a plus.
• Strong analytical skills, deep expertise in credit risk analysis, and the ability to exercise sound independent judgment.
• Excellent written and spoken English and Chinese; Fluency in Mandarin is advantageous.