As the use of data analytics grows increasingly sophisticated and nuanced, it might be incumbent for your firm to hire a “data scientist,” someone who can sift through and interpret complex data sets. Such people are in high demand, and they are commanding large salaries. But before you hire one, be forewarned. Just because the potential hire has an exemplary academic and work background doesn’t mean he or she will be an ideal fit for the position.
In an article on the Harvard Business Review blog written by Michael Li, founder and executive director of The Data Incubator, a New York-based startup, Li advises companies seeking to hire a data scientist to ask job candidates if they produce analytics for humans or machines.
The answer to this question will help firms determine if candidates will thrive — or fail — in the position.
Li explains that when analytics are produced for machines, the computer is the decision maker and consumer. The data scientist creates models that “act on their own, choosing which ads to display, making recommendations to users, or automatically trading in the stock market, often in less than the blink of an eye.”
These kind of data scientists need “exceptionally strong mathematical, statistical, and computational fluency to build models that can quickly make good predictions,” Li says.
Data scientists who produce analytics for humans, on the other hand, use data to tell a story, says Li. “Because they have to explain their results to others — particularly those who are not as well versed in data science — they might deliberately choose simpler models over more accurate but overly complex ones,” he notes. “They have to be comfortable drawing higher-level conclusions — the ‘how’ and ‘why.'”
Source: HBR, The Question to Ask Before Hiring a Data Scientist