Irvine, California, United States
• In Python and SQL, developed a novel classifier using ultrasonic time series data to detect 50+ false negatives weekly, reducing errors by 15% in Segment Anything Model for real-time object detection for food spillage. • Collaborated with business stakeholders to pinpoint and mitigate the root causes of false negatives, leading to strategies that are projected to save $1.3 million and reduce product spillage across conveyors by 32.5%. • Built ETL processes on SAP data using SQL/Python to identify inefficiencies with KNN/network graphs. Saved 2.2M in cost