New eBook

A Leader’s Guide to Generative AI Skills in the Workplace

What mature tech teaches us about new career opportunities in AI
Home Blog What mature tech ...

What mature tech teaches us about new career opportunities in AI

Careers in data and AI: what mature tech teaches us about new opportunities

Technology continues to evolve, and with it, opportunities expand as roles become more specialized. For those considering a career shift into the data and AI space, it only takes a brief look into the past to see its potential. 

30 years ago, coding was a highly-specialized skill that a small group of people had within highly-technical companies or the government. Today, every engineer codes–mechanical engineers, electrical engineers, material scientists–everyone is coding. You go to a campus, everyone is coding.

Then there is software engineering. Back in its infancy, when you wanted to build a software engineering team, you just hired software engineers. Full stop. With the tools and programming languages that have been developed over time, this no longer is the case. Now you hire for front end, back end, dev ops, security, mobile, and the list goes on. We learned how to build a team based on the projects we wanted to deliver. 

Just as the skill to code became horizontal, and the roles within software engineering evolved, I believe that data and AI will be the same. In truth, it’s already happening. People know there is not just one general data scientist to hire–there are data scientists, machine learning engineers, data engineers, ML Ops engineers, and so on. It’s starting to become a proper, mature job category with more diverse and specialized roles within them. 

Additionally, 5 years ago, data scientists would be specific on statistics and probability, but today everyone needs a little bit of proficiency in working with and cleaning data. I believe this will eventually become a horizontal skill as coding did. 

So what is the right career for you? It depends on your background. 

If you are a mechanical engineer who is good at linear algebra, it doesn’t make sense to go be an ML Ops engineer, you would be better suited as a data scientist. If you are a software engineer, don’t try to reinvent yourself to be a data scientist, become an ML Ops or data engineer because you already have 60% of the skills that are needed to do those roles. 

It’s about pattern-matching your background to where you want to go in your career, and there are ample opportunities for someone looking to get into the field!

Understand. Develop. Mobilize

Unlock the full potential of your workforce

Learn how Workera can power digital transformation and produce measurable results across your enterprise.

Get a Demo