Building a large data science team is hard. It’s still a nascent practice, and there are no models to follow. Much of what works for established departments like engineering, BI/analytics, and other functional areas does not apply to data science. Don’t force this decidedly square peg into a round hole. Instead, take time to understand the nuances of how a data scientist thinks, works, and thrives and design something completely appropriate for them.
Through his unique experiences of building several large data science teams (80+ people) at companies like Stitch Fix and Netflix, Eric Colson has learned a few things. These lessons aren’t obvious and could have only come through experience, trial and error, and iteration. Join Eric as he recounts some valuable lessons learned, including:
Some of these may seem unintuitive, and they may not apply to all cultures or organizations. But understanding the nuances may inspire new thinking that allows you to unlock the potential of your data science team.
Eric Colson is chief algorithms officer at Stitch Fix, where he leads a team of 100+ data scientists and is responsible for the multitude of algorithms that are pervasive to nearly every function of the company, from merchandise, inventory, and marketing to forecasting and demand, operations, and the styling recommender system. He’s also an advisor to several big data startups. Previously, Eric was vice president of data science and engineering at Netflix. He holds a BA in economics from SFSU, an MS in information systems from GGU, and an MS in management science and engineering from Stanford.
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