Seattle, Washington, United States
Business & operations-focused data analyst with experience in pricing, forecasting, and cost analytics, grounded in real-world supply chain and ERP-driven processes across the U.S., Europe, and Asia.
Supporting data center operations and cross-functional program execution within Microsoft Cloud Operations.
• Negotiated vendor contracts using cost and on-time delivery KPIs, achieving 15% fewer returns and $400K annual savings through performance benchmarking. • Drove a cross-functional collaboration with logistics and compliance teams to implement sustainability KPIs, providing analytics support for global ESG reporting and vendor performance. • Led ERP system redesign as cross-functional product owner, aligning design, product, and engineering teams to modernize workflows and data integration; improved usability, consistency, and cross-department efficiency by 20%. • Integrated purchase, return, and vendor data to uncover root causes of returns; collaborated with sourcing and design teams to resolve supplier issues and reduce return rate by 10%. • Developed SQL + Looker studio dashboards to track inventory turnover and procurement, identifying slow-moving SKUs and reducing excess inventory by 12%, improving replenishment accuracy.
• Conducted EEG-based analysis of human–robot interaction, revealing that literary engagement elicits more relaxed and nuanced facial expressions; insights informed affective-response modeling for social robotics. • Synthesized emotion–response modeling insights for adaptive computing systems., establishing conceptual groundwork for socially intelligent systems such as Alexa and other adaptive robots.
• Designed and prototyped Python-based information-retrieval models for a European open-data portal, using click-through feedback to enhance search accuracy and dataset discoverability. • Executed A/B tests on search-interface prototypes, analyzing user queries and click behavior to drive iterative portal development and enhance usability.
• Designed and optimized computer vision pipelines for real-time sports video analysis using OpenCV and TensorFlow, accelerating object-tracking by 0.7s per frame. • Fine-tuned YOLO models with Adam and Padam optimizers for multi-angle highlight detection, boosting recognition of players, balls, and jerseys across frames. • Streamlined benchmarking and model-deployment workflows across GPU and CPU environments, enhancing reproducibility and testing efficiency for production deployment.