ML at The Home Depot with Pat Woowong: The Falloff Model and Lead Scoring

When people think about The Home Depot, they probably think more
about lumber
and tile than they do ML models.  Sure, there
is plenty of lumber.  But machine
learning also plays a key role in the business, in places that
customers can see as well as the behind-the-scenes
operations.Senior Content Advisor Q McCallum met up with Pat
Woowong, Director of Data Science at The Home Depot, to explore
how the company mixes their very rich dataset with domain
knowledge to employ machine learning deep inside the
business.  To frame this, he walked me through the Falloff
model and Lead scoring, two projects that his team deployed to
address the unique challenges of a company that handles both
retail and services.During our conversation, we discussed:
understanding where models fit into the bigger business picture;
using expert domain knowledge to drive feature selection and
feature engineering; the value of process; and, to top it off,
what it's like to work at The Home Depot.Other places to find
Pat:


LinkedIn: https://www.linkedin.com/in/patwoowong/

"How THD keeps shelves stocked using ML" (the talk he
mentioned during our interview):
https://twimlai.com/podcast/twimlai/how-ml-keeps-shelves-stocked-home-depot-pat-woowong/

"The Value Proposition for Using ML in Brick-and-Mortar
Retail Stores: Home Depot"
https://www.youtube.com/watch?v=rF8jtdX-hGo



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