How to build a data science team
There is good reason that Googling the phrase “Data science is a team sport” brings up so many entries. The skills that are required of a data scientist are so varied that it would be practically impossible to find them in a single person.
And even if you could find somebody that embodies all those qualities, you will likely have to pay top dollar for them. But let’s assume that you are not in a large market like North America, and your search grows even more impossible. That was the challenge faced by Vodafone NZ (New Zealand) when analytics and data strategy manager David Bloch faced in building a data science team, who presented his saga before the Teradata Partners conference this week.
In a country of less than 5 million people, it shouldn’t be surprising that the national telecom provider would likely be the one that has the largest Hadoop cluster and most ambitious big data analytics program. According to Bloch, the traditional HR approach for finding specialists with years of experience just won’t work when you’re in a small market. “They would probably filter out the people you want to talk to,” Bloch said.
Instead, he called for adopting a startup mentality, combing events like meetups and hackathons for people whose interest and enthusiasm outweighed their actual experience. Bloch had a good idea of what he was talking about given his experience with several data-related startups prior to joining Vodafone.
Bloch defined a series of roles for populating his data science team, encompassing engineers, hackers, analyst, statistician, story teller, and change agent. The roles are not necessarily mapped to individual positions; for instance, the analyst and change agent, or the hacker and engineer, could be the same person.
More specifically, the engineer is the team’s “automation magician.” Someone who comes with a DBA or ETL background, this is the person who works with the hacker to build data flows, and ensures that technically, the trains run on time. On many teams, this would be called the data engineer.
