Abstract: 

For a growing data organization, inbound requests for ad hoc data analysis is a good sign. It means people are eager to make data a central part of the business, and they trust your team to help them do it. And it’s an incredible opportunity to drive impact! But data teams frequently don’t grow as quickly as the companies that surround them, so how can you avoid becoming the victim of your own success? Reddit’s Data Science team found itself in this situation as the company was in rapid growth mode, with roughly 50 employees to each data scientist. This created an environment of constant microdistraction as the team attempted to keep up with the demand for pulling data and ad hoc analysis, and this distraction ultimately caused more problems than the ad hoc work was solving. The solution? Formalize a procedure for assigning and triaging emergent work via a process called Data Science On Call, in which members of the Data Science team have prescheduled periods where they are responsible for tackling all inbound requests. Since rolling out the process, the Data Science team has dramatically improved its efficiency, found capacity to support teams it never would have worked with otherwise, and increased the happiness of individual contributors. This talk will describe how on call rotations can be used to turn the weakness of distraction into your greatest strength. It will explain how to use an on call rotation at your company to proactively solve recurring issues, expose new hires to broad datasets and crossfunctional partners, and give team members uninterrupted time to tackle projects that are aligned with their career goals.

Bio: 

Katie Bauer is a data scientist and engineer based in the San Francisco Bay Area with experience in search, digital advertising, online retail and consumer web. She currently works at Reddit, where she was a founding member of the Data Science team. She currently supervises the Data Science and Analytics On Call program, and has worked on everything from building analytics and experimentation infrastructure to modeling user behavior to managing of the data science internship program.