Job postings for “data scientists” have sharply declined and have been eclipsed by those seeking “data science” skills, according to my quick analysis of trends on job-posting site Indeed.com. It’s hard to draw too much insight from a keyword comparison on a single website, but I’ll try: The debate over definitions and job titles is coming to an end and employers just want people who can do the job.
|“Data scientist”, “Data science” Job Trends||“data Scientist” jobs – “data Science” jobs|
The job, in many cases, is probably some subsection of the amalgamation of skills — SQL, Hadoop, statistics, machine learning, programming, etc. — the data community has been claiming for years that a data scientist should possess. I think many employers finally realize they need someone who can do some of these things, but they might not need someone who can do them all — and they likely don’t want to call that person a data scientist. That’s probably because the job entails more than just experimenting data for the sake of it, so titles such as analyst, engineer, product manager and the like are actually a better fit.
A comparison of postings for specific data science skills kind of backs up this hypothesis. Listings for “statistics,” “Hadoop” and “machine learning,” for example, all predate the data science craze, peak at around the same time “data scientist” listings peaked, and now are on the decline. However, listings mentioning broad terms such “data science” and “big data” are on the rise. (And by the way, Amazon appears to be doing a lot of hiring for people with data science skills.)
|“Data scientist”, “Data science”, “big data” Job Trends||“data Scientist” jobs – “data Science” jobs – “big Data” jobs|
Perhaps employers are finally getting wise to the realities of hiring in these fields. Employees coming into companies to work with big data or do data science are going to touch a lot of technologies and apply a lot of techniques, and possibly for a lot of different customers. There’s not a lot of use artificially filtering out candidates upfront with job titles they don’t think they can live up to or skills they might not possess.
The unicorns are going to Google, Facebook and whatever startups have the best stock options. That means other companies — even venerable ones such as Booz Allen Hamilton — are seeking people who meet some rough, but probably undefined, profile and, more importantly, can learn the things they don’t already know.
Of course, I could by way off base. Please share your data science and big data hiring experiences in the comments.
Image copyright Shutterstock / Sergey Nivens.
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