The Saturation of Data Science

Jay F.

Remember when the term data scientist first came out in early 2010? Harvard Business Review coined it as the The Sexiest Job of the 21st Century and prompted everyone who ever worked in statistics or analytics to immediately back-change their positions and titles for recruiters on Linkedin madly searching for these so-called unicorn data scientists. The joke at the time was that a data scientist is actually a data analyst that lived in the bay area. The other one was that a data scientist was a statistician plus an extra $100K.

Fast forward a few years later and now these very same statisticians are being paid $150K to $300K per year in total compensation. Don’t believe me? Check out the different salary charts here on Levels.fyi.

I mention these stats because it helps illustrate a point of career achievement in software engineering and data science in the past decade. The originations of both crafts that were never regarded as highly respected compared to traditional paths within medicine, law, or banking, have now switched to become the easier standard commodification of a successful career path. All it takes is a decent undergraduate degree here, or bootcamp completion there, and a nice cushy internship over there, with an eventual guaranteed outcome for stable income, regular hours, and a sling-shot into the upper middle class.

We are entering the saturation of the data science market. Not in job demand but rather as a career that is now in the purview of the general public. While ten years ago it was the machine learning enthusiast and analytics fiends that paved the creation of the data science job by doing what they loved. We have now headed to the point where freshmen in college decide on the data science career out of the rejection of other disciplines.

There’s nothing wrong with that. But as a data science and analytics candidate within the system, the best way to stand out towards companies and startups hiring will always be the appreciation of the curiosity and analyses in the field itself.

A successful data scientist is not measured by the academic rigor or other optimizations a perennial 4.0 student can make. Rather it’s the push towards continuous curiosity of using data to unravel why certain people get cancer, why houses get sold for a million dollars, why people enjoy reading about fake news, and why people love working in data science.

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