Everyone knows that any job in the field of data science has been almost universally declared to be part of the career of the future. That’s because data is the new currency, and whoever controls it, masters it, and uses it to derive the most valuable insights will control the future of the industry. That new reality has led to a pronounced spike in demand for those with skills in a variety of STEM-related fields of study, with a particular emphasis on data science and data scientists with a Ph.D.
As the spike occurred, hiring managers and corporate technology organizations scrambled to build out new technical teams to create their fledgling data science operations, in most cases without any real pre-existing infrastructure. It didn’t help that some of the systems that businesses looked to develop to meet their analytics and data science needs were either immature or had to be designed from the ground up, placing an ever-larger premium on established data scientists with the requisite skills to build new tools out of whole cloth.
A High-End Industry is Born
All of those factors led to the growth of an industry that was top-heavy, starved for talent, and most of all – expensive. Even entry-level data scientists with a Ph.D. could expect to earn north of US$100,000 per year, which is quite a high sum compared to their peers in other professions. Alongside internal corporate data operations, a cottage industry of data science contractors sprang up overnight, and they too found no shortage of new clients. All told, the big data and analytics industry is already on a path towards a total value of $275 billion by 2023, and that doesn’t even account for whatever technological developments might accelerate growth between now and then. The bottom line is it’s a great time to be a data scientist, for today at least. As for tomorrow? Perhaps not.
The Ground Shifts
Although data science as a concept isn’t going away anytime soon, it’s an industry that’s not immune to the laws of supply and demand. There’s already evidence that indicates that the makeup of the labor market within the data science sector is already different today than it was even two years ago, and that’s something that should pique the interest of every aspiring data scientist out there. To begin with, a deep dive into the market found that as of 2018, 48% of data scientists (individuals employed in that role) hold a Master’s degree, rather than a Ph.D., which already represents a significant change in the market.
More interestingly, though, is the fact that 15% of today’s data scientists have only attained a Bachelor’s level education. That figure is indicative of an industry with labor needs that have shifted substantially within a very short time. If you follow the trend line in the industry, it’s already possible to foresee a future where Master’s level candidates will be fighting over a dwindling number of positions, which will exert downward pressure on salaries. When you pair that conclusion up with data that indicates that enrollment in graduate-level data science programs has been growing, you have a recipe for a glut within the labor force happening in the very near future, and that doesn’t account for the elephant in the room – automation.
The Casualties of Progress
If you look at today’s data science industry, you will see a booming field with plenty of opportunities. If you overlay the educational trends outlined above, it doesn’t change today’s picture, but it does point to a future with lower barriers to entry and with fewer specialized, high-knowledge roles. That in and of itself isn’t a sign that the industry is contracting, but rather that it is becoming standardized, accessible, and widespread. There’s still likely to be plenty of demand for lower-skilled, entry-level data science jobs, which will still command more than respectable salaries. The problem is that the industry doesn’t exist in a bubble.
As the data science industry has grown, another parallel industry has grown alongside it – artificial intelligence (AI). As AI has developed and grown, by leaps and bounds in some areas, it has become ever more disruptive in a variety of industries, and the data science industry is no exception. Already, industry analysts expect that 40% of data science tasks will be automated by the year 2020. That’s going to further accelerate the democratization of data science by allowing just about anyone to work with data (at least in a basic way) with little to no prior expertise. Initially, that will free up trained data scientists to pursue advances in the field, but as the Ph.D.-holding pioneers in the field will attest – there’s only so much room in the market, no matter how fast it grows.
The Bottom Line
It doesn’t take sophisticated predictive analytics to read the tea leaves for the data science industry. As with any other new, high-growth field, it’s going to continue to boom for some time, but it will look very different for those inside it within a few short years. That’s not terrible news for those that have already entered the industry and started to build out résumés filled with accomplishments. For them, data science will remain a rewarding and lucrative profession for years to come. They will need to prepare for a future where it won’t be as easy to switch jobs or work their way upwards in an organization, but there are worse things to be than well-paid and secure.
For today’s aspiring data scientists, however, the road ahead may not be so easy. Although there will always be a need for data scientists at the high end of the educational spectrum, those that pursue advanced degrees won’t have their pick of jobs forever, unless they’re able to contribute to pioneering approaches or can substantially add to the quality of current techniques. Below them, however, the industry will be moving further towards candidates with no more than a Bachelor’s degree, before automating even those positions out of existence.
The takeaway here is that despite the hype surrounding data science careers, they’re not going to continue to produce the same outcomes as today for much longer, and will eventually succumb to AI disruption like every other industry. As any economist will tell you, the market forces of supply and demand are absolute, and as any AI aficionado will tell you, no job is going to remain safe for much longer. It’s impossible to say when the wave will crest, but it will happen. For any aspiring data scientist, that’s a data point that would be disastrous to ignore.