The Care and Feeding of Data Scientists

Introduction

Over the past 5 to 10 years, data science has grown tremendously. But as young as data science is as a discipline, the craft of managing data scientists is even younger. Many of today’s data science managers were thrust into management roles out of necessity (“battlefield promotions”) or because they were the best individual contributors, and many come from purely academic backgrounds. At some companies, engineering or product leaders are being tasked with building new data science functions without any real data science experience of their own. More and more people find themselves managing data scientists without the necessary toolset or role models or mentorship to do the job well.

This report aims to fill that gap—to become a resource that data science leaders (whether they’re data scientists or engineers or product managers) can use to understand how data science management is both similar to, and distinct from, other types of management and to learn concrete tips for building and sustaining their teams. It’s also aimed at anyone trying to decide whether managing data scientists might be for them someday.

Getting data science leadership right is important. Data scientists are difficult to hire. But too often, companies struggle to find the right talent only to make avoidable mistakes that cause their best data scientists to leave. The statistics on data scientist retention bear this out. In surveys, data scientists report that they stayed at their previous jobs ...