There will be no data science job listings in about 10 years, and here is why. There are no MBA jobs in 2019, just like there are no computer science jobs. MBAs, computer science degrees and data science degrees are degrees, not jobs. I believe the reason companies are hiring people into data science job titles is because they recognize there are emerging trends (cloud computing, big data, AI, machine learning), and they want to invest in them.
There is evidence to suggest this is a temporary phenomenon, though, which is a normal part of the technology hype cycle. We just passed the Peak of Inflated Expectations with data science, and we are about to enter the Trough of Disillusionment. From where I stand, the end result will be that yes, data science as a degree and as a capability are here to stay, but the job title is not.
The coming Trough of Disillusionment with data science job titles will be the following:
Automation is coming for many tasks data scientists perform, including machine learning. Everymajorcloud vendor has heavily invested in some type of AutoML initiative.
A recent example of a similar phenomenon can be seen in system administrators. This used to be one of the hottest jobs in IT during the pre-cloud era, but in looking at Google Trends from 2004 until now, you can see how active directory, a key skill for systems administrators, has swapped positions with AWS. In a recent article by job site Dice, it mentions several tech jobs in danger of becoming extinct. One of the prominent jobs going extinct is Windows/Linux/Unix systems administrators. Those positions are being eliminated by the cloud, DevOps tools, and DevOps engineers. I believe something similar will happen to data science job titles. The role of data scientist will change into something else.
Does this mean data science is a bad degree to get? I believe it will be a very important degree in the next 10 years, but it will not be a job title. Instead, there will be an evolution. The takeaway for data scientists is to look toward improving their skills in things that are not automatable:
Applied domain expertise
Creating revenue and business value
Some future job titles that may take the place of data scientist include machine learning engineer, data engineer, AI wrangler, AI communicator, AI product manager and AI architect. The only thing that is certain is change, and there are changes coming to data science. One way to be on top of this trend is to not only invest in data science and machine learning skills but to also embrace soft skills. Another way is to think about tasks that can be easily automated -- feature engineering, exploratory data analysis, trivial modeling -- and work on tasks that are harder to automate, like producing a machine learning system that increases key business metrics and produces revenue.
Companies that want to be ahead of the curve can embrace the pragmatism and automation of machine learning. Becoming early adopters of cloud or third-party software solutions that automate machine learning tasks is a clear strategic advantage in 2019.