Interview with Khalifeh Al Jadda, Director of Core Data Science at The Home Depot

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Hello and welcome to the AI Today podcast. I'm your host Kathleen Mulch. And I'm your host bottled schmelzer Our Guest today is Kelly fellow who is the director of core data science at the Home Depot Hai Khalifa. Thank you so much for joining us on AI today. Hi guys. Thanks for having me. It's my pleasure. Yeah, welcome Khalifa and thanks so much for joining us. We'd like to start by having you introduce yourself to our listeners. Tell them a little bit about your background and she current role at the Home Depot. Sure. So my name is Kelly fell Jetta. I have PhD degree in computer science. I started my career in data science back in June 2013 as a PhD intern at Careerbuilder, which is one of the largest job boards in the US and the my career with Career Builder actually took extended to until 2018 during that I was actually leading the search and recommendation data science team where I was lucky actually need to get involved early enough and building the semantic search engine for the company and after that building an AI based recommendation engine dead. So the semantic search engine actually is the one that has been leveraged by the company for their be to be sort of business and the day I guess recommendation engine which we built their home is now serving millions of job-seekers on the BTC side of the company. So very proud of that Journey with Career Builder in 2018. I joined Home Depot and I joined as a senior manager, of course recommendation data science team under the online business of Home Depot, I build the team and we actually worked very hard and the last two years to build again state-of-the-art e-commerce recommendation engine for Home Depot, very proud of what we accomplished as a team found in May this year twenty-twenty. I was promoted to director of course data science in my organization. Now, I have the court search data science team called recommendation wage. Science team and the visual AI team our focus our my route Focus now is as the name suggests to improve the core functionality of homedepot.com home from search and documentation perspective. So we work to improve sexual even see we work to make our recommendation more and more personalized and relevant to our customers and guide our customers and kind of give them the experience which they get in the physical store as part of our interconnected experience initiative. So that's overall. What am I roll includes now at Home Depot and I'm very proud and excited actually about the team that we have built for the core data science at Home Depot on the work that we have done that for the e-commerce, you know, that's that's fantastic. And you know, I I really have to give a plug for the talk that you gave at the data for a i week online conference because you you showed you age. And about thirty forty minutes really walking in Fairly good detail how the Home Depot actually does its product recommendation system. We showed how the system works. There was some math in there, which is great all the time a little bit of code more math than code showing how it was the song and it was fantastic. I mean and so, you know for those who are listening if you really wanted to to dive deeper and see this the presentation you can the the conference is available for free. So if you go to data a icon did a i c o n f c o n f, and look for a Khalifa's presentation page, it's on the e-commerce system and talks about the recommendation system. It's just fantastic and I love seeing it because you know, I have to say I'm you know, probably like many of us here in the United States now have a big Home Depot customer feel. I feel like I go there like every other week, especially, you know, we're all at home these days so you can't help but notice the things that you need to write a fix and repair right and they even do some stuff outside job. And it's it's it's the season of the deer kind of eating everything and Wrecking everything. So so I think it's fantastic what maybe maybe for our listeners here? If you can provide a little bit of insight you talked a little bit about the recommendation system. I know that it's really hard to we don't have slides here on a podcast that's going to be hard to share. But you were talking about solving challenging e-commerce problems using the power of data science as a Todd the title of the talk. So maybe you can share some of the insights that you shared at the conference around the recommendation system round recommendation systems in general maybe around the relationship between data science and e-commerce, which you know, maybe people haven't thought about that deeply Yeah, sure sure. And first of all, thank you for highlighting the talk. Absolutely. It was actually a great conference overall. So I congratulate you guys on the success of the conference just enjoyed being part of it. Thanks for having me back to the question about the talk and the relationship between the e-commerce and and the data science absolutely data size is transforming retail to the boss really on the e-commerce side and how we do things and the e-commerce and they use cases I presented in my talk. We're actually real use cases of things that we implemented at Home Depot on faith and that changed actually How We Do recommendation on our websites to make them more relevant and to make them as they mentioned earlier and more personalized to our customers need. So

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