Blair YANG

· Risk Modeling: Developed quarterly Expected Credit Loss (ECL) prediction models using Python (Pandas, Statsmodels), integrating macroeconomic indicators with loan performance data; improved forecast reliability by 30%.

· ETL Automation: Engineered end-to-end data pipelines (Python + SQL) to automate data consolidation and model input preparation; reduced processing time from 2 days to 0.5 day a

Developed quarterly Expected Credit Loss (ECL) prediction models using Python, improving forecast reliability by 30%. Engineered ETL pipelines to automate data consolidation, reducing processing time from 2 days to 0.5 day and cutting data errors by 50%.

· Process Optimization: Enhanced digital customer journey for Cross-Border Wealth Management Connect; reduced in-branch visits by 30% and indirectly reduced manual data entry errors.

· Cross-Functional Coordination: Served as liaison between business, technology, and compliance teams, ensuring system changes complied with KYC/AML regulatory standards.

Enhanced digital customer journey for Cross-Border Wealth Management Connect, reducing in-branch visits by 30%. Served as liaison between business, technology, and compliance teams, ensuring KYC/AML regulatory compliance.

Developed Python-VBA tool to automate client document collection and compliance checks, reducing onboarding time by 50%. Introduced automated data consistency checks and exception alerts, reducing manual review errors by 30%.

· KYC Automation: Developed Python-VBA tool to automate client document collection and preliminary compliance checks, reducing onboarding time by 50%.

· Data Quality Assurance: Introduced automated data consistency checks and exception alerts using Python, reducing manual review errors by 30%.

Supported competitive analysis and customer surveys for Stock Connect-related digital services, identifying a 20% potential revenue opportunity that informed project decisions.

· Feasibility Analysis: Supported competitive analysis and customer surveys for Stock Connect-related digital services; identified a 20% potential revenue opportunity that informed the project's go/no-go decision.

· Supply Chain Optimization: Applied Python for logistics optimization modeling, conducting real-world testing and iterative code improvements to enhance supply chain efficiency.

· Data-Driven Solutions: Developed predictive models for inventory management, resulting in 15% reduction in logistics costs through optimized routing and scheduling.

Applied Python for logistics optimization modeling, enhancing supply chain efficiency. Developed predictive models for inventory management, resulting in 15% reduction in logistics costs.

· Academic Focus: Machine Learning, Blockchain, Quantitative Modeling.

· Capstone Project: Developed Python-based backtesting tool with performance evaluation using Sharpe, Max Drawdown, and other risk metrics.

Pursued a Master of Science in Financial Technology with a focus on Machine Learning, Blockchain, and Quantitative Modeling. Developed a Python-based backtesting tool for performance evaluation using Sharpe ratio and Max Drawdown.

· Academic Focus: Database Management, Financial Systems, IT Project Management.