Highly driven Master's candidate in Applied Statistics and Data Science with a proven record of orchestrating AI-driven Deep Learning and Computer Vision initiatives in high-traffic retail environments. Demonstrates exceptional skill in managing end-to-end operational workflows, from defining project scope and coordinating large-scale data ingestion to implementing real-time video analytics for anomaly detection. Effectively bridges the gap between technical and operational teams, working closely with store management and compliance to integrate Computer Vision models at self-checkout lanes—resulting in reduced shrinkage and notable operational improvements. Combines robust machine learning expertise with practical project management experience, ensuring seamless collaboration and measurable outcomes. Now seeking an ML Engineer role that leverages both advanced technical acumen and extensive operational insight to deliver impactful, scalable solutions.
Orchestrated the deployment of AI-driven Deep Learning and Computer Vision technologies within retail environments, focusing on operational workflows. Partnered with cross-functional teams to define project scope, coordinate large-scale data ingestion from Verint software, and manage the real-time annotation and validation of video analytics. Implemented computer vision models at self-checkout lanes to detect anomalous transactions, effectively reducing theft and enhancing overall loss prevention. By fostering seamless collaboration among technical staff, store management, and compliance teams, achieved notable improvements in accuracy and operational efficiency.
Researched and resolved data discrepancies in collaboration with troubleshooting teams, ensuring data integrity and minimal project disruptions. Served as the primary contact for client service teams across multiple initiatives, overseeing both front-end and back-end data management to minimize errors and maximize accuracy.