Looking for a unicorn? Get in line. Actual data scientists are in high demand, and there's not enough of them to go around. If you want to identify the right talent, consider these tips.
If your company is trying to hire a data scientist, proceed with caution. Given the shortage of data science talent, more candidates are assuming the title hoping to command a higher salary. Actual data scientists are much harder to find, and they're harder to keep because they're in high demand.
"The way I define a data scientist is somebody who knows programming better than a statistician and more statistics than a programmer. Both of those traits are table stakes," said Anthony Goldbloom, cofounder and CEO of data science competition platform Kaggle, in an interview.
Business domain knowledge is also important, since data scientists need to understand the problem they're solving and its context. Increasingly, organizations recruiting data scientists are also looking for machine learning experience, since the capability is necessary to keep pace with data growth, particularly with the addition of IoT devices. Data scientists should also be, but aren't always, effective communicators.
"You have to understand how to talk to people in a way that's simple and comprehensible to them while maintaining accuracy," said Alexander Isakov, CEO of business data solutions and strategy firm Pallantius. "CEOs and senior management don't care if we use a random forest or Oracle Delphi. As long as we clearly explain what's going on and how to make it actionable."
Everyone wants to hire unicorns -- those rare beings who are equally good at math, statistics, computer science, domain knowledge, communication, and perhaps machine learning. Since hiring a unicorn is difficult at best, organizations need to make compromises. They need to be mindful of the compromises they're making and why.
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