This post is based on personal experience from working in the field and having interviewed junior candidates sporadically for the last three years (None had a PhD, only bachelor’s or master’s). The thoughts are my own and may well not be the case in other situations.

I recently answered a question on Quora about whether or not it is getting harder to get into data science, and it garnered some attention, which made me realize it might not be overly clear on the (from my perspective) current state of how industry views the in recent years very much hyped position of a “Data Scientist.”

A few years ago, it became common knowledge that having data scientists would outperform competitors, simply because it was the “sexiest job of the 21st century,” and the big four had committed to a so called “war on acquisition,” where — even before graduation — the leading data-driven companies were offering more and more lucrative offers to students doing machine learning or applied statistics or something else arbitrarily related to data science. At the time, the position of a data scientist was as vague as it was sought after. This “war on acquisition” wasn’t as much driven by a need as it was by not letting their competitors require this supply of hyped up talent.

Granted, the big four likely knew what they where hiring since they where the leading titans in those fields; but the unrealistically high offers students where getting, and the easy of which they got the jobs, lead to a mainstream hype that data science is something you can achieve with a few years of uni without prior industry experience. The result was that universities got a huge surge of demand for data science educations. Universities complied and started new programs and specializations to accommodate for the vast amount of young adults who wished to get on the hype-train of data science.

Now, just about now, plus minus one or two years, this huge influx of students who got on those programs are about to graduate, more or less at the same time that the majority of the industry (outside of the leaders in the field) are getting to realize they’re actually not getting what they hype led them to believe. Data science is hard, requiring years of experience in multiple areas of expertise. A data scientist nowadays is common knowledge in the industry to be a unicorn; something of course everyone wants, but realistically are not likely to find; or they find one but have no way to offer the market price of what a unicorn is worth.

Instead what has happened in recent years is more well-defined specialized roles, that has easier-to-specify expectations and requirements. This definition of requirements have let to the realization that these positions are over-payed, and the supply of talent is growing every year. Roles such as “machine learning engineer,” “deep learning algorithm engineer,” “data engineer,” etc. has spread the expectations of the unicorns across multiple positions, and realizing that each of these positions are really hard to find qualified people for.

This trend is likely to continue for a while, which is good for the industry, but likely demoralizing for possibly a great many newly graduated students who struggled for years to graduate believing it would land them a lucrative position, when instead reality shows up and say they need an additional five to ten years of colorful industry experience to be considered for anything other than intern positions.

Just as getting a three year education to become a software developer won’t land you a position paying you much more than rent and a subway card in the first years, a data science education won’t automatically land you the “sexiest job of the 21st century”; unless you challenge that fucker called Life and keep going. As in any industry, it takes a long time before being considered “good.”

Best of luck, and remember: you only fail if you quit! Thanks for reading.