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More and more businesses are looking for employees with skills in the emerging field of machine learning. Recruiting those employees, however, can be difficult, given how in-demand they are. LinkedIn on Tuesday shared some data that illustrates the typical career path of a machine learning professional, providing insight into how an organization can build up the talent it needs.
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To conduct its analysis, LinkedIn studied the profiles of members from around the globe with at least one machine learning skill listed on their profile. It analyzed these profiles from April 2017 through March 2018.
The analysis showed what other skills these professionals most commonly shared, and when in their career they learn these skills. Based on when a professional adds a skill to his LinkedIn profile, the skills most commonly developed right before machine learning are data mining, Python programming proficiency, and righteous R skills.
Knowing this, LinkedIn suggests, recruiters could search for job candidates -- externally or within their own organization -- that have these skills, and then help them develop machine learning skills.
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Other skills commonly shared, but which are learned earlier on in a professional's career, include Java and C++, common programming languages.
The data also shows in what industries you can find machine learning talent. Perhaps not surprisingly, one third of professionals with machine learning skills are in higher education and research. Just over one quarter of ML professionals are in the software and internet industries, while the rest are distributed across other industries.
LinkedIn suggests that organizations should look outside of their own industries to find the right machine learning candidates. Last year, according to the data, 22 percent of people with ML skills changed jobs. Among those who changed jobs, 72 percent also changed industries.
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The data also suggests how a recruiter can find the right candidate by looking at the combination of skills a machine learning professional has. For example, ML professionals who moved into the finance and banking sector were more likely than other ML professionals to have business strategy, SAS and Tableau skills. ML professionals who moved into the software industry were more likely to have a broad range of programming skills.
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