{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"The NeuralPod","title":"Shreesha Jagadeesh - RecSys at Scale, Leadership and Retail Personalisation","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/175ee446\"></iframe>","width":"100%","height":180,"duration":4625,"description":"The NeuralPod welcomes Shreesha Jagadeesh, Associate Director of Applied Machine Learning at Best Buy, for an insightful conversation. Shreesha shares her extensive career journey, from his early days in biomedical diagnostics to her current role at Best Buy. The discussion delves into machine learning techniques in retail, the evolution of recommendation systems, and the technical challenges of personalisation at scale. They also explore his contributions to HR tech at Amazon and his innovative paper on homepage personalisation using XGBoost, soon to be published at RecSys.Additionally, Shreesha offers valuable advice on leadership, career growth, and navigating the competitive field of machine learning. Get ready for an episode packed with expert insights and practical tips for aspiring AI professionals.00:00 Introduction and Guest Background01:48 Career Journey: From Academia to Industry03:34 Transition to Software and Data Science04:19 Consulting and Managerial Roles07:09 Joining Amazon and HR Tech09:35 Advising a Startup in India11:19 Joining Best Buy and Recommender Systems12:46 Challenges in Retail Personalisation28:28 Implementing XGBoost for Homepage Personalisation40:01 Top-Down and Bottom-Up Approaches in AI40:57 Challenges in Implementing Recommender Systems42:20 Understanding Business Objectives in AB Testing44:36 Experimentation and Value Demonstration46:20 Representation Learning in Machine Learning51:03 Leadership Principles in AI58:08 Hiring and Team Building in AI01:02:43 Future of Recommender Systems and Generative AI01:03:00 Upcoming Book on Recommender Systems01:10:21 AI Tools for Productivity01:16:20 Conclusion and Final ThoughtsReferences: Multi stage recommender systems blog https://eugeneyan.com/writing/system-design-for-discovery/Hidden technical debt in machine learning https://papers.nips.cc/paper_files/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.htmlHSTU...","thumbnail_url":"https://img.transistorcdn.com/S9UsaicIOGkusb_qg9ue2EkbA-peZPxoBVz-S26BLrA/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS82MDNj/NTUyN2ZkNGU1OTZj/MDlhMTZiYTE5ZDUx/ZTJlYS5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}