Assessing the fastest-growing job is tricky. For example, using job-posting data isn’t great because there could be lots of duplicate postings out there or a lot of the postings could be going unfilled. Another big issue is defining exactly what a job is: The exact same responsibilities could be associated with the job title “data scientist”, “data engineer” or “ML engineer”, depending on the particular job titles a particular company decides to go with. So, whoever’s evaluating job growth is going to end up bucketing groups of related jobs and responsibilities into one particular, standardized job-title bucket, probably these days in a largely automated, data-driven way; if you dug into individual examples, I’m sure you’d find lots of job-title standardizations you disagreed with but some kind of standardization approach is essential to ensuring identical roles with slightly different job titles get counted as the same thing.
One approach to assessing job growth that I came across recently that I thought was decent was done by so-called “Economic Graph” researchers at LinkedIn. They examined millions of jobs started by LinkedIn members between January 2022 and July 2024 to calculate growth rates for each job title. They set — but didn’t disclose in their methodology — a minimum count threshold so that some super-rare job that, say, grew from 1 to 100 doesn’t show up in their results. Yeah, the job grew 100x but is so rare that it’s not significant when looking across millions of jobs. They also had other thoughtful exclusions like internships, volunteer positions, interim roles, student roles and jobs where hiring was dominated by a small handful of companies.
At the time of recording, LinkedIn had generated country-specific reports for quite a few different countries, namely: Australia, Brazil, Canada, France, Germany, India, Indonesia, Ireland, Israel, Italy, Mexico, the Netherlands, Saudi Arabia, Spain, Sweden, Switzerland, Türkiye, the UAE, the UK and the US.
I’m going to start by diving a little into the results from the US and then I’ll generalize globally a bit afterward. To dig into these results yourself in their full detail, you can check out the link we have in the show notes. Scroll all the way to the bottom of the US results to access links to all of the other countries’ reports.
Without further ado, and perhaps not a surprise to many listeners given the topics we discuss regularly on this podcast, the fastest-growing job in the US is AI Engineer. Based on data from all LinkedIn users, the report provides helpful summary information on each job. For AI Engineer, for example, it shows that LLMs, NLP and PyTorch are the most common skills; that the top cities for AI Engineers are San Francisco, New York and Boston; and that the most common roles current AI Engineers transitioned from are full-stack engineer, research assistant and data scientist. If you’re thinking of making a career change and flexible work is important to you, the report also provides info on that. For example, AI Engineering roles in the US are fully remote 36% of the time and hybrid 27% of the time, suggesting that only about a third of AI Engineers in the US are expected in the office every day.
Now, if AI Engineering doesn’t sound like a fast-growing job you’d be interested in, I have good news for you because the second-fastest growing job in the US is also highly relevant to a lot of this podcast’s listenership because the the second-fastest growing job in the US is AI Consultant! This role isn’t necessarily as technical as AI Engineer with top skills including prompt engineering and the top role transitioned from being operations associate. While some AI Consultants would no doubt be as technical as AI Engineers, there’s also room in the AI Consultants’ tent for folks who are more commercially oriented, operations oriented, management oriented and/or product oriented.
The next chunk of jobs aren’t obviously relevant to our listenership with job titles like security guard, event coordinator and physical therapist showing up in or near the top-ten fastest-growing jobs in the US, but scrolling down to #12 we find AI Researcher, which is squarely relevant to this podcast’s audience. AI Researchers are concerned with advancing AI algorithms themselves and so might often be even more technical, specialized or academic than an AI Engineer and perhaps be less directly concerned with production AI deployments. AI Researchers’ most common skill is deep learning and, interestingly, these AI Research roles mostly require in-office work. Only 11% of AI Researchers work fully-remote and only a further 19% have hybrid working arrangements.
Looking beyond the US, AI roles are proliferating in other countries as well. For example, like in the US, AI Engineer is the #1 fastest-growing role in the UK and the Netherlands. AI Engineer is also the fifth fastest-growing role in Sweden, the sixth fastest-growing role in Canada and Israel and the 12th fastest-growing role in India. AI Researcher jobs are also proving popular abroad — for example, it’s the third fastest-growing role in Canada and Israel (even more popular than AI Engineering in both countries!), and ninth fastest-growing role amongst the Dutch.
Interestingly, the job title of “data scientist” itself — a title that someone with all the responsibilities of an AI engineer might have been very likely to have only a few years ago — has clearly ceded its formerly high-growth position to these more AI-specific job titles like AI Engineer, AI Researcher and (in the US at least) AI Consultant. Indeed, as I alluded to earlier, data scientists are amongst the most common job titles transitioning into AI Engineer and AI Researcher roles. Data science skills aren’t any less important than five years ago or ten years ago, but as AI proliferates we’re seeing more and more specialized subtypes of data scientist emerge.
If you’re interested in learning more about AI Engineering, I highly recommend checking out Episode #847 with Ed Donner, which we released in late December. Ed is extremely knowledgeable and well-spoken about what AI Engineering entails on a day-to-day basis and how you can hone AI Engineering skills. Or, if you’re more into books, you can check out the outstanding author Chip Huyen’s brand-new book, which is aptly titled AI Engineering; we’ve got a link to that for you in the show notes.
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