Santa Clara, California, United States
A Data Science professional with proven expertise in leveraging advanced analytics and machine learning to drive strategic improvements across semiconductor, bio-tech, logistics, and financial sectors. Demonstrated success in optimizing operations, enhancing decision-making, and boosting efficiency through innovative data solutions.
Managed and governed master data in Oracle ERP, ensuring 99% data accuracy for supply, demand and capacity planning, while reducing BOM, sourcing, routes and product attribute discrepancies by 15% to improve supply chain efficiency in semiconductor manufacturing processes. • Developed automated dashboards using Tableau & PowerBI, cutting manual data reconciliation time by 30%, enhancing visibility into supply chain performance, and improving issue resolution time by 20%. • Collaborated with IT and business teams across upstream (Agile, Cognos, QPMD) and downstream (Oracle, Kinaxis, De-mantra, Model-N) systems to streamline master data workflows for NPI and existing products, reducing Engineering change order updates by 25% and improving product releases by 10%.
Played a crucial role in a data migration project to SAP, utilizing SQL and Python for data manipulation and ETL processes, ensuring seamless data integrity and accessibility while achieving a 10% reduction in data processing time. Enhanced decision-making efficiency by 16% through the development of real-time Power BI dashboards, enabling precise tracking of KPIs and improving pharmaceutical production strategies. Implemented statistical analysis and predictive models with R to optimize production processes, resulting in a 9% increase in operational efficiency and a 6% reduction in equipment downtime for the Plant Engineering team.
Leveraged Tableau to create real-time dashboards and Python for advanced data analysis, optimizing routes and carriers to achieve a 12% reduction in logistics costs and enhance decision-making. Used Advanced SQL and Excel to refine inventory and order management systems, boosting efficiency by reducing order processing time by 17% and decreasing time-to-market by 20%. Developed a web scraping system using Python for the Americas region, enhancing competitor analysis and market trends, which improved pricing accuracy by 11% and bid success rates by 10% for global air cargo RFQs. Managed time-sensitive, customs-bound global logistics projects with a 95% on-time completion rate, enhancing supply chain strategies through improved collaboration and communication.
Utilized advanced machine learning models in R to refine credit scoring and risk assessment for various financial products, successfully reducing credit risk by 8%. Optimized Advanced SQL queries for 20% faster customer data retrieval and manipulation, deployed interactive Power BI dashboards which led to a 14% increase in user engagement. Enhanced forecasting and budgeting by 18%, optimizing financial planning across sales, operations, and marketing, directly improving product efficiency and responsiveness.