Empowering Big Teams to Take on Even Bigger ML Challenges (Jan Neumann, Executive Director, Machine Learning, Comcast) episode artwork

EPISODE · Dec 14, 2021 · 30 MIN

Empowering Big Teams to Take on Even Bigger ML Challenges (Jan Neumann, Executive Director, Machine Learning, Comcast)

from Data Science Leaders

Managing a large enterprise team of data scientists can be a complicated undertaking. There are so many opportunities, big and small, to serve the business with AI and machine learning. How do you ensure your teams are focused on the big picture without getting bogged down in the minutiae of the day to day?Jan Neumann, Executive Director, Machine Learning at Comcast, leads a team of about 300 data scientists, divided into eight different focus areas. If anyone knows how to manage a large data science team, it’s him.In this episode, he shares his strategies for effectively managing a team of this scale in the enterprise. Plus, he explains why he prioritizes continued learning, and shares tips for building out a feature store.We discuss:- Managing large data science teams at scale- Making time to gain knowledge from the ML community- What a feature store is and why data scientists should careMentioned during the podcast:- The Idealcast with Gene Kim- Mik + One with Mik Kersten- a16z Podcast- Yannic Kilcher on YouTubeTune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Managing a large enterprise team of data scientists can be a complicated undertaking. There are so many opportunities, big and small, to serve the business with AI and machine learning. How do you ensure your teams are focused on the big picture without getting bogged down in the minutiae of the day to day?Jan Neumann, Executive Director, Machine Learning at Comcast, leads a team of about 300 data scientists, divided into eight different focus areas. If anyone knows how to manage a large data science team, it’s him.In this episode, he shares his strategies for effectively managing a team of this scale in the enterprise. Plus, he explains why he prioritizes continued learning, and shares tips for building out a feature store.We discuss:- Managing large data science teams at scale- Making time to gain knowledge from the ML community- What a feature store is and why data scientists should careMentioned during the podcast:- The Idealcast with Gene Kim- Mik + One with Mik Kersten- a16z Podcast- Yannic Kilcher on YouTubeTune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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This episode is 30 minutes long.

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This episode was published on December 14, 2021.

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Managing a large enterprise team of data scientists can be a complicated undertaking. There are so many opportunities, big and small, to serve the business with AI and machine learning. How do you ensure your teams are focused on the big picture...

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