Huiming Qu didn’t plan to work in data science, a nascent field at the time she was pursuing a Ph.D. in computer science, but one course in data mining changed all of that. She started her career in the research department at IBM, transitioned to a 50-person startup, spent some time in the financial services industry, and today leads data science and machine learning in the marketing and online functions at The Home Depot.

In this episode, Huiming explains the similarities and differences between her previous experiences and her current role, in which she is tasked with helping customers more easily find the products and services they need as they embark on home improvement projects. (And who hasn’t started at least one of those since the COVID-19 pandemic shifted many of us to working from home?) She also outlines some of the challenges of managing a data set of over 2 million product SKUs and getting pilot programs to market quickly, and she explains why she champions the need for cross-functional teams to execute complex technology projects. Read the episode transcript here.

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Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Sophie Rüdinger.

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Guest bio:

Huiming Qu leads the online data science and platform team enabling search, product recommendations, real-time personalization, visual shopping, and various other innovations for The Home Depot’s digital channels. She is a technical leader with deep expertise in artificial intelligence, data science, engineering, and product leadership, with a proven record of driving billion-dollar contributions with scalable machine learning solutions and strategic innovation. Qu has more than 10 years of experience managing large AI and data science programs at IBM’s Watson research lab, Distillery, and American Express. She earned a Ph.D. in computer science from the University of Pittsburgh; holds six patents and has others pending approval; and has published more than a dozen academic papers around data management, machine learning, and optimization.

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