7 Careers in a Lifetime? A System Predicts Your Next Best Move

women deciding their best career move

Ever feel like the world is changing faster than it used to? That’s because it is. 

As we enter into National Science Week, WiTWA takes a look at some new research coming out of Australia and what it might mean for the future of work.

1970’s New York Times bestseller and gripping read Future Shock scientifically lays out the evidence for the rate of technological and societal change rapidly increasing – and this pace has only sped up over the 50 years since its publication shocked the world. 

What the rate of change looks like in the jobs market

If you think your job is in danger of being taken over by AI – that’s because it probably is. For a fact check, almost 60% of students in Australia are being trained in occupations that will be widely automated within 10-15 years.

If you have hopped roles, companies, or careers far more often than your parents – then you’re not alone. Back in 1975, workers over 45 averaged around 10 years in a job, these days, that figure is 6 years, 8 months. We’re averaging 7 careers per lifetime.

But does that spell doom and gloom for future job seekers? That would be a no. But what it might mean is that we get a machine to help us find new roles – after all, couldn’t AI potentially be a better predictor of job success than a career counselor?

Using AI to better predict your best career move

With all this knowledge in hand, a great idea, and data from 8 million job ads, researchers at the University of Technology Sydney have developed a machine learning system to give insights into the best career transition recommendations – based on your current profession.

The new research, with a system dubbed Skills Space and published in PLOS ONE, is designed to help job seekers determine which occupations closely align with the skills they have acquired in their current (or previous) profession for maximum potential success in a different career. By finding jobs with similar skill sets, and/or with low barriers of entry to learning a given skill, they are able to show jobs in which an applicant is more likely to succeed. The study covers 6981 skills across jobs and matches between them using Revealed Comparative Advantage to determine which are more similar.

Using job-to-job transitions data from the Household, Income, and Labour Dynamics in Australia survey, they were able to create a machine-learning algorithm to “predict the probability of an occupational transition from the ‘source job’ to the ‘target job’ occurring or not.”

The system also determines the probability of each job’s risk of automation, which can identify which job transitions are not a clever choice. Via the Job Transitions Recommender System, you can see which jobs are close fits, determine which have growth in demand for workers, and then identify which skills to invest time and professional development efforts into for the most successful transition.

This sort of system is likely to be extremely important in our near future. Leveraging the power of data and AI will help us to make better career decisions, know where to concentrate our studies for the best chance of success, and keep on top of where career trends are heading as it applies to our future. For industry, it’s a great way to help with employee retention through retraining efforts.

We’re here supporting the future of jobs

With rapid technology growth and access to vast amounts of information our societies are changing, which means our jobs are changing. WiTWA’s Techtrails initiative is helping young people in WA secondary schools discover the STEM careers of the future, highlighting new roles and exciting opportunities like AI Intellectual Property Negotiator, Urban Technologist, Genetics Coach, Lifelog Education Advisor, and Digital Tailor. We want women to know that STEM is an exciting space, where career diversions can take you anywhere and everywhere. And with a little bit of help, it’ll be an amazing, lifelong learning journey.

Article written by Julia Sinclair-Jones

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