About 10 years, there will be no data science job and MBA jobs. Here is why. MBAs, computer science degrees and data science degrees are degrees, not jobs.
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.
The coming Trough of Disillusionment with data science job titles will be the following:
- Many data science teams have not delivered results that can be measured in ROI by executives.
- The excitement of AI and ML has temporary led people to ignore the basic question: What does a data scientist actually do?
- For complex data engineering tasks, you need five data engineers for every one data scientist.
- Automation is coming for many tasks data scientists perform, including machine learning. Every major cloud 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.
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 automatize:
- Communication skills
- 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.