- Developed machine learning and statistical-based prediction models to enhance the reliability of spend and volume forecasts, optimizing value capture (~$4B) and operational efficiency.- Developed a categorization logic for contingent labor Time & Materials (T&M) spend, enabling effective management of approximately $100M in annual T&M spending by category.- Provided resource bill rate analysis by region, title, and level for Contingent Labor negotiations, uncovering inflated supplier-proposed baselines and establishing new benchmarks, avoiding at least $37M in annual contingent labor cost increases.- Conducted analysis of catalog demand and average product prices from different suppliers, estimating potential savings of $0.7M to $1.5M by adding potential suppliers to the catalog, thereby supporting supplier addition decision-making.

• Applied statistical techniques including ANOVA, A/B testing, and MSA to extract valuable insights from production datasets, contributed to a 11% improvement in product yield rate• Co-developed innovative patents in touch panel technology, leveraging data-driven approaches to enhance yield rate, TW Patent No. 110106548/ No. M611352

• Utilized trend chart analysis and log file review to proactively identify potential equipment failures, resulting in a decrease of 7% in machine downtime and improved equipment availability through adequate parts preparation• Implemented the Machine Father system on the YS-380G production line at TSMC Fab12 and led a team of 10 engineers• Directed and trained local engineers on latest model of equipment, resulting in a 13% faster transition compared to previous generation• Awarded customer recognition for reducing scheduled maintenance downtime by 16% for customer equipment through the removal of redundant items and optimization of the maintenance pipeline