Keep up with the latest hiring trends!
LinkedIn has released new data around how machine learning professionals pivot their careers, to help companies better find and recruit this talent. Machine learning is already one of the most in-demand skills, and demand is growing fast.
Use skill pathways to decide where to upskill.
LinkedIn analyzed members with machine learning skills to see what other skills they most often share, and the most common order in which they develop those skills (i.e., add them their LinkedIn profile).
- The candidates and employees best suited for ML upskilling may have data mining skills, Python programming proficiency, and righteous R skills. These are the skills most commonly developed right before machine learning (which makes sense, since Python and R are often cited as two of the top languages for machine learning.)
- Skills learned early on include HTML, Java, and C++
Look beyond your own industry to expand talent pools.
Professionals with ML skills aren’t shy about jumping to a new company.
- Last year, 22% of people with ML skills changed jobs, according to LinkedIn data.
- 72% of those ML-skilled folks who changed jobs last year also changed industries.
- Those with ML skills tend to be clustered into a few industries, with one-third (33%) in higher education and research.
Adjust your search to include professionals with transferable skills.
By looking at the top skills held by machine learning talent pivoting into your industry, you can find those who may be more likely to join your company.
- For example, people with machine learning skills who pivoted into the consulting industry tended to have skills that could translate ML findings into strategic business decisions.
- ML pivoters heading into the software industry, on the other hand, were much more likely to have a broad range of programming skills.
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