IT Best PracticesIT Staff & Team Building

How to Build a Great Data Science Team

Is your business one of those ambitious types seeking to create an “analytics culture?” Do you want your business to be one of those businesses? Chris Nerney has an article at Computerworld taking a look at what you need to build a great data science team to support that fabled culture.

Analyzing the Team

One challenge of building a data science team is that data scientists are supposedly in short supply. Another challenge is deciding what constitutes the right balance of skill, background, and personality. In order to address that second point, what you must first do is define a clear business goal. At the very least, you can then answer whether or not a given resource will help toward that goal.

There are two ends to data science, and each side requires a different edge. Those producing analytics to learn about consumers are expected to have strong mathematical and computational skills, whereas those who inform C-level executives about operational decisions must have stronger soft skills. However, these “communicators” still need to know their way around the numbers:

“They don’t need super coding skills, but they need to be able to access data,” [Claudia Perlich, chief scientist at Dstillery] says. “They need at least a scripting language, say Perl or Python, in order to manipulate data once it’s out of wherever they found it. And they need a practical understanding of statistics. They don’t need probability theory, but they need to understand empirical distributions of data and how the mean can be super misleading when you have a long-tail distribution.”

According to Kevin Lyons, senior vice president of analytics at eXelate, every data project has four components—understanding the business need, gathering and preparing data, doing the modeling, and operationalizing the outcome.  Toward that end, you want a healthy mix of communicators, statisticians, and coders. One last, almost comic point of advice is that you do not need your data scientist to understand your industry. As Perlich says, anyone smart enough to become a data scientist is going to be able to figure out your industry in no time at all.

You can read Nerney’s original article here:

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