Building the Tools You Need to Excel at Data Science with Home Depot

Data Science intimidates some and invigorates others. Unstructured and structured data can provide insight into the root of systemic problems and disclose the information needed to create a solution. Building tools to collect, curate, cleanse, interpret, and apply data enables corporations to create these solutions using open source and proprietary technology.

Over the last two years, developers, data scientists, and leaders from the Home Depot have invested time, resources, and expertise in building the tools you need to excel at data science. 

In January 2021, Kelly Seibert who supported the Home Depot eCommerce Data Science team joined Women Who Code Senior Leadership Fellow Naomi Freeman, and Women Who Code Community Manager Sapphire Duffy for a ‘Data Science Lunch and Learn.’ Technologists received live instruction on using deep learning, predictive modeling, NLP, pattern recognition, and computer vision. The Home Depot's Online Data Science team shared how they implemented solutions to surprise and delight customers with Search, Recommendation, and Visual AI. 

In April 2021, Felicia Rives, Technology Director of Merchandising at The Home Depot presented, ‘A Fresh Coat of Paint: How The Home Depot Refreshed the Customer Shopping Experience.’ Felicia dove into a technical challenge with multiple layers of complexity that she solved with her cross-functional technology team during the pandemic. 

The Challenge: Prior to the pandemic, a Home Depot team was focusing on how to enhance the functionality of buy online, pickup in-store (BOPIS) capabilities for the paint department. When the pandemic hit and associates pivoted to remote work, a new layer of complexity was added to the challenge – including shifting customer behaviors. 

In June 2021, Huiming Qu, Vice President of Data Science, E-Commerce, and Marketing at The Home Depot joined Madeleine Shang, Women Who CodeData Science Legacy Track Lead for a fireside chat about 'Reimagining Leadership in Machine Learning.' They discussed how the move to decentralized teams has changed the nature of work, the incorporation of my team members from nontraditional paths, and forward-thinking communications and its importance in leadership. 

In October 2021, Women Who Code Data Science hosted the Online Data Science Team, Jinzhou Huang, Vrishali Bhandare, Yao Sun, and Megan Forrester for, ‘Data Science at The Home Depot.’ Technologists or career transitioners interested in what a data science leader does hear what they can expect to do in the role. This video also includes three lightning talks about Scorecard-based Model Performance Index, Reinforcement Learning, and Marketing Data Science.

Using data to solve problems is not a new concept. Being able to identify, organize, and apply ethically sourced information to make life better will be integral to fuel future innovation. Preparing to transition to data science doesn’t have to be overwhelming with the right resources, network, and experts like The Home Depot Online Data Science Team guiding you.

More From the Podcast
Stephanie Rideout, Python Leadership Fellow at Women Who Code, interviews Jinzhou Huang, Director of Data Science at The Home Depot.