Owned the development and deployment of a production machine learning model to identify false in-stock signals — items that appear available in inventory systems but are actually missing from shelves. This high-impact retail inventory challenge was turned into a scalable inference solution, helping prevent approximately $13M in annual lost sales by enabling targeted corrective action at store level.