Big Data at Work: Dispelling the Myths, Uncovering the Opportunities by Davenport Thomas H

Author:Davenport, Thomas H. [Davenport, Thomas H.]
Language: eng
Format: epub
ISBN: 9781422168165
Publisher: Harvard Business Review Press
Published: 2014-02-04T00:00:00+00:00

Retention of Data Scientists

Once they are hired or created, companies may also face issues in retaining data scientists. Several of those I interviewed in online firms or small start-ups had changed jobs several times in the past year. One commented, “After about a year, it often becomes clear that there is nothing left for me to do.” (Presumably, that data scientist is brought in for a single project, and it is done after a year.) Another noted, “Data scientists receive lots of job offers—sometimes I get two or three calls a week from headhunters. It’s not surprising that with so much opportunity there is a lot of movement.”

While I know of no studies on how to retain data scientists, the usual approaches—money, relationships, good bosses—are probably effective. If you are from a large company and have hired data scientists, make sure that they are not only working directly with business functions and units, but also with other data science and analytics people. Given the strong need for intellectual stimulation and growth, however, the most important way to retain a data scientist is to provide him or her with good data and interesting problems to solve.

In my interviews with data scientists, the issue of impact cropped up frequently. They want to use data to have a substantial impact on the world. They view this as a unique period in history in which there are huge data sets and very powerful tools. As Amy Heineike, a prominent data scientist at the start-up Quid in San Francisco, put it in an interview:

If you have access to the data and the tools, you can already find out some really cool stuff, but we are just scratching the surface. What inspires me is the opportunity to create something really interesting. Could something be important, have impact, or reach a lot of people? I am also interested in how to include data scientists within a diverse engineering team and company, and in combining the diverse skills that make up an effective data science team. So when I evaluate an opportunity, I look for a rich data set that the company has to work with, or an important question for which we might be able to find data. And I want to make sure that there are the resources and senior management support available to support the data science function.13

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.