In recent weeks, I’ve interacted one-on-one with nearly 100 Data Science job candidates for a handful of open positions at TROVE – a growing and successful predictive data analytics start-up where I am the Managing Director of Data Science.
I started the search thinking that I was looking for a “needle in the haystack” and proceeded in typical fashion choosing from a standard set of questions to drive candidate interviews. (1) What is your educational background? (2) What is your work experience? (3) What kinds of data and with which industries have you worked? (4) How proficient are you in R? Python? C++/Java? (5) Are there code and technical report samples that you can share? (6) Has your work resulted in the commercial launch of the product?
In the not too distant past, these questions were enough to gain a rough appreciation for a candidate’s experience, skills and potential fit with the company. It was unusual to find candidates who could broadly and deeply range across these topics.
What I discovered, to my surprise, is that today nearly every candidate “made the grade”! I needed new criteria to have any hope of fairly and accurately ranking candidates.
I landed on a set of four new questions that consistently led me to discover what it was that I was really looking for– the Scientist within the Data Scientist:
Armed with these additional questions, I was able to identify candidates who have what I need most on my teams: a passion for discovery combined with a rigorous scientific approach to the insightful application of modern statistical analysis and machine learning methods.