Data Scientist are pack animals - they need a team to develop and achieve their maximum productivity. Leaving them as individual fighters will turn them inefficient, stuck too often, lost in complicated projects - and most likely lead to churn. But how do you build a Data Science team? How do you achieve a good skill mix? And how do you create a culture of creativity, open-mindedness and productivity? There is not the "one size fits all" answer, but at least there are some clear guidelines.
Having a performing Data Science team is a common aspiration of many companies nowadays. But only building an organizational unit with persons having "Data Scientist" on their business card and e-mail signatures doesn't make a Data Science Team. Too often newly founded teams are facing serious problems in creating impact.
Data Scientists in a nutshell
Before thinking of a whole team, you'd need to understand the nature and background of Data Scientists. Event though the one Data Scientist profile doesn't exist, at least typical "clusters" or sub-profiles are typically observed. Based on the individual "skill mix" and preferences you'll observe at least six of them:
- Data Researcher
- Business Data Scientist
- Machine Learning Engineer
- Data Engineer
- Data Science Manager
I dedicated a separate article to this important topic: Chasing unicorns →. The main takeaway that is important hereafter: Data Science involves many different activities and requires a multitude of different skills. No single Data Scientist can cover them all - so build a team with the right skill mix.
General challenges
| Machine Learning Engineer |
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Skills:
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