When hiring data scientists, there’s nothing more frustrating than making the wrong hire. Data scientists are in notoriously high demand, hard to attract, and command large salaries — compounding the cost of a mistake. At The Data Incubator, we’ve talked to dozens of employers looking to hire data scientists from our training program, from large corporates like Pfizer and JPMorgan Chase to smaller tech startups like Foursquare and Upstart. Employers that didn’t have good hiring experiences in the past often failed to ask a key question:
Is your data scientist producing analytics for machines or humans?
This distinction is important across organizations, industries, and job titles (our fellows are being placed at jobs with titles that range from Quant to Data Scientist to Analyst to Statistician). Unfortunately, most hiring managers conflate the types of talent and temperament necessary for these roles.
While this isn’t the only distinction among data scientists, it’s one of the biggest when it comes to hiring. Here’s the difference, and why it matters: