"In fact, we're looking for all kinds of skills and backgrounds as we look to build our team at Intel — from programmers to those with creativity, curiosity and those with great communication skills," he adds. "It's rare to find a "data unicorn" that can do it all, and we're not spending our time recruiting for such a talent. We build out teams to reflect a variety of backgrounds and experience, which brings greater insight to our data analytics work."
Getting hands dirty with data
Because there's no one-size-fits-all path to becoming a data scientist, it can be difficult to identify good candidates. Rogers' advice is to look for individuals that can show their mettle by getting their hands on a data set — perhaps from Kaggle, DataKind or the government — building up a data analytics environment and telling a story with that data. And individuals interested in pursuing data science should take it upon themselves to seek out data sets to work with.
"Get your hands on data and do something with it," he says. "There are big data sets out there, some of them sufficiently ugly enough to give you some real experience working with data. Take a big data set, put it into an environment where you can really do something with it and answer a simple question. You don't need to do anything too technically crazy. When you work with data that's messy, you start to see where data sets go wrong. That is the moment you start to speak the data scientists' language."
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