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HBO and The Ringer's Bill Simmons hosts the most downloaded sports podcast of all time, with a rotating crew of celebrities, athletes, and media staples, as well as mainstays like Cousin Sal, Joe House, and a slew of other friends and family members who always happen to be suspiciously available.
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I sat down with Scott Golder, Senior Director of Data Science at Home Depot, to talk about what actually works when you're building data teams. Scott has spent 20+ years fixing what others couldn't, from Capital One to running algorithms behind one of the top five e-commerce platforms in the world. He breaks down why deeper academic backgrounds don't always make better data scientists, how to make AI trustworthy at scale, and what happens when you fall in love with your methodology instead of the problem you're supposed to solve.
This conversation gets into the messy reality of deploying machine learning in the real world. Scott shares how Home Depot uses recommendations differently than selling sweatpants, why human accountability can't be replaced by 10,000 AI coworkers, and which AI tools he actually uses when his kids go to bed. If you're building data products or trying to figure out where AI fits in your business, this one's for you.
Chapters:
- 00:00 - The Importance of Seasonality
- 01:43 - Connecting Data Science with Real-World Challenges
- 10:17 - Understanding Customer Empathy in Product Design
- 16:15 - Transitioning to AI and Machine Learning
- 21:41 - Navigating Accountability in AI Decision-Making
- 25:00 - AI in Everyday Life: Personal Experiences and Insights
- 33:10 - The Future of Software Engineering and AI
- 38:14 - The Importance of Data Governance in AI
Companies Mentioned
Home Depot
Capital One
IBM
Duolingo
Guest Information
Scott Golder is Senior Director of Data Science at Home Depot, where his team powers the algorithms behind one of the world's top five e-commerce platforms. He previously helped scale data science at Capital One and has a background in sociology, linguistics, and computer science. Scott specializes in building data teams that blend academic depth with real-world implementation in hostile corporate environments.
Key Takeaways
Academic credentials don't predict data scientist performance. Fall in love with the problem, not your methodology. AI works best for summarization when you fence the data. Human accountability can't be replaced by software. Speed and cost of AI models dictate where they're feasible. Your data foundation must be solid before AI can help.
23 episodes
