The Science of Managing Data Scientists

Case Studies, Data Science Great America Ballroom J
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Creating great products powered by big data can be challenging.

Data science work is often ambiguous, which can make results unpredictable and scheduling almost impossible.

Many of the popular software engineering processes just won’t work for these innovative and ambitious projects. Waterfall falls apart; it just doesn’t make sense to define the product before understanding the limitations of the data and technology. And shoehorning data experiments into tight agile sprints is both difficult and doesn’t necessarily lend itself to discoveries that involve a lot inspiration and perspiration before a light bulb moment.

Even with a working process, few teams collaborate truly effectively. Projects that involve machine learning, algorithm development, or other deeply technical endeavors, are filled with advanced math and complicated terminology, which leaves plenty of teams with communication gaps that prevent the synergy realized when working cohesively.

Thankfully there are solutions to these problems! Based on personal experience, and interviews with many other leaders spearheading big data initiatives, this session aims to distill these lessons into actionable strategies you can use to improve process and communication for your own team.

Photo of Kate Matsudaira

Kate Matsudaira

Decide, Inc.

Kate Matsudaira is an experienced CTO and has spent the last 7 years immersed in startups in architect and CTO roles. Prior to joining the startup world she spent time as a software engineer, and technical lead/manager at Amazon and Microsoft. Kate has a passion for big data and all the challenges that come with it. She has lot of experience building large scale distributed web systems, with some mobile, cloud computing and engineering leadership thrown in for good measure. She maintains a blog at


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