Most of us have either conducted an interview for a prospective data scientist hire or been interviewed for a data scientist position. A lot of the focus on data science interviews focuses on the common technical questions, and rightly so.
However, sometimes the key factors for the potential fit are solidified by answers to the non-technical questions, which themselves can quickly evolve into highly technical answers. Some of the more useful non-technical interview questions are listed below:
What is the fundamental role of a data scientist? Can you give an example?
The main reason I love this question (or question set) is it’s a great indicator of whether someone can explain highly technical work to a very non-technical audience, except in kind of reverse-format. Instead of taking something technical and framing it in general terms in order to be comprehensible to the audience, this time they’re starting with something very general and gradually working up to some potentially highly technical concepts.
Their domain experience and communications skills are just as important as their technical skills. -Wayne Thompson
Starting from conveying a high-level understanding of why data scientists are valuable and then distilling that down to a concrete yet still decipherable example, all on the fly, is a skill that many employers would say is lacking in most of the candidates they evaluate.
Additionally, by asking the second part of the question in asking for an example, you get a chance to get an early impression of whether the candidate is very strong on marketing but lacking on substantive and concrete experience.
If you join, what’s the first question you’d probably ask our head of data science?
This question is a bit more off the beaten path, but again could provide some unique perspective on what the candidate is all about. If they reply with some canned response like ‘what does your average day look like,’ that’s not necessarily bad.
However, sometimes a candidate will have a very perceptive question that demonstrates far more knowledge of the relevant domain than you had thought, and this knowledge might not have been brought to light during the standard data scientist interview.
Which data scientists do you admire most?
This is an interesting question and could go many ways. For example, the candidate might be a top data scientist but then not really keep up on the individual news of what other data scientists are doing.
They could be caught a bit off guard with this question, and how they react when they don’t know an answer that they think they should know is very telling. Seeing how someone acts in a moderate pressure situation and thinking on the fly is a highly useful data point in this age of canned interview questions and responses (not to mention interview coaches).
On the other hand, if they start naming off some of the more conventional names (with no one that you haven’t heard of), you might ask a follow-up question about what specific aspects of the work do they admire?
This is where you can potentially start to get an idea of whether the candidate has a tendency to confidently discuss items that they really aren’t at all familiar with.
The data scientist who is openly willing to say ‘I don’t know’ is unfortunately becoming a more rare commodity, with a more common reaction being to start using big words to hopefully draw attention away from their lack of knowledge in a specific area.