Data Science in the Enterprise: Insights from eBay, Stitch Fix, Teleon Health, and RISELab
We recently hosted a panel discussion with several data science leaders about organizational design and tooling for enterprise data science. Watch the full video:
The MIT Club of Northern California is running a year-long, monthly series of data science and AI events for alumni and guests. Domino hosted “Data Science in the Enterprise,” a discussion covering topics relevant to organizations using data science to develop products, insights, or efficiencies.
The panel included a variety of folks from different industries and company sizes:
- Moderator: Mark Chew, Co-Founder at Teleon Health
- Hoda Eydgahi, Data Science Manager at Stitch Fix
- Eric Jonas, Postdoc at Berkeley's RISE Lab
- Zoher Karu, former Chief Data Officer at eBay
- Eduardo Ariño de la Rubia, Chief Data Scientist at Domino Data Lab
Each panelist spoke with Mark individually for a 15-minute fireside chat. All panelists came together at the end of the evening for a Q A session.
While the panel covered a variety of topics, there were two primary themes:
- Organizational design of data science groups.
- Making wise choices for tools and techniques for the specific problems your company faces.
In the organizational category, panelists discussed where data science teams should report in an organization, how to manage and measure data science work, and how to organize larger teams with specific roles.
When covering tools and techniques, panelists cautioned that most problems don’t require fashionable tools and techniques like distributed computing or deep learning. The vast majority of data science work is solved by fundamental techniques and basic tools.
Our thanks to the MIT Club of Northern California and Zetta Venture Partners for putting together the event, and to each of our panelists for sharing their experiences in building data science practices in the enterprise. You can learn more about Domino, or read how enterprises customers are using Domino to conduct faster, reproducible data science.
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