Prakhar Mehrotra shares Walmart’s digital transformation journey and explains how the company is using recent advancements in machine learning to power core retail operations like pricing, assortment, and replenishment. Along the way, Prakhar demonstrates how to leverage human expertise and use it as feedback to improve your algorithms.
This session is sponsored by Walmart Labs
Prakhar Mehrotra is senior director of machine learning for retail data science at Walmart Labs, where he overseas the research and development of pricing, assortment, replenishment, and planning algorithms to help merchants take smarter decisions. Previously, he was head of data science for finance at Uber, where he led a global team of data scientists and data analysts spread across Amsterdam, Hyderabad, and San Francisco in the research and development of machine learning algorithms related to financial forecasting (supply and demand), budget planning, and economic simulations for autonomous vehicles. He also worked on research and development related to payment analytics and treasury financial simulations. Before that, he was a senior data scientist on the sales and monetization team at Twitter. He holds an advanced engineering degree in aeronautics from California Institute of Technology (Caltech) and a dual master’s degree in aeronautics and applied mechanics from École Polytechnique, Paris, and Caltech. He is a peer reviewer for CVPR, ICCV, and AAMAS and has given numerous invited talks including as a keynote speaker at the EARL conference, the Toronto Machine Learning Summit, Deep Learning Summit, the NYU Center for Data Science, and the Wharton Technology Conference. He also chaired the session on forecasting at a 2017 international symposium on forecasting in Australia and was a judge for risk and intelligence at the European Fintech Awards in Brussels.
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