Let's say that your company has finally grown into a position to hire a Data Scientist. Awesome. But there's a problem. You aren't a Data Scientist yourself. As an entrepreneur, you need to hire experts in domains that you yourself are not an expert, so how do you do that? Specifically, how do you interview someone for skills you yourself do not posses?

This is the challenge that I brought to Kronos, a global leader in workforce management solutions, and they were generous enough to allow me to ask two of their experts: Alex Krowitz, a Senior Research Engineer, has a background in neural networks. And Tom Walsh, Data Scientist, received his Ph.D at Rutgers in machine learning. They came up with a number of good suggestions, and were able to narrow it down to three important interview questions:

Take me through (start-to-finish) a recent project you've done with data
Have the candidate reverse engineer a problem that you've already solved.
In front of you is a data set, what could you tell decision makers from this data?

In all of this, you're looking to mitigate the risk of hiring a half (or quarter) of a true data scientist. There are many different parts to consider. You might find someone who knows how to take numbers and build a predictive model; but being a data scientist is so much more than that. From the ability to work with people, for example, to collect the data and understand what it means, to choosing the data models correctly to explaining the meaning to management and key decision makers. By being clear on what data-driven decisions are most important to your business, you're much more likely to discover the right data scientist who fits your needs.