This is 5-Minute Friday on the fastest growing jobs of 2024. Welcome back to the Super Data Science Podcast. I am your host, John Krohn. Let's start things off with a couple recent reviews of the show as we often do on Fridays.
This first one's from Michael Luncharich. I'm probably butchering your last name there. Sorry, Michael. But Michael's a gene editing expert from St. Louis, Missouri, or in St. Louis, Missouri, at least. He says, thank you for the podcast. I am changing my career from scientist to data scientist. Your podcast has been extremely helpful in my learning process and discovering new cool tools like Lightning AI and XGBoost. Awesome, Michael. That's very cool to hear. I hope we can continue to support you.
on your journey to data scientist another recent review comes from brad edwards who's a technical product manager in british columbia in canada he says that he's a huge fan of my work it's highly technical accessible humanist and optimistic he says he loves the way that i give guests a ton of space to explore and share so that we get the uh the guest voice as well
Thanks a lot, Brad, for that as well. I'll continue to try to remain optimistic and strike that balance of technical and accessible at the same time, which is a tight rope to walk. Thanks to everyone for all the recent ratings and feedback on Apple Podcasts, Spotify, whatever podcasting platform you use, as well as for the likes and comments on our YouTube videos. If you provide a written review on Apple Podcasts, I will be sure to see that.
and I'll be sure to read it on the air like I did today's reviews. Okay, on to today's topic, which is the fastest growing job. 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 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 just 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 the so-called Economic Graph Research Team 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, although they didn't disclose in their methodology, a minimum count threshold so that some super rare job that, say, grew from 1 to 100 from 2022 to 2024, that that, you know, doesn't show up in their results. Because, yeah, that job grew 100x, but it's so rare that it's not significant when looking across millions of jobs. It's not generalizable or useful information.
Generally useful information. The LinkedIn team also had other thoughtful exclusions like excluding internships, volunteer positions, interim roles, student roles, and jobs where hiring was dominated by a small number of companies. At the time of recording, LinkedIn has 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, Turkey, the United Arab Emirates, the United Kingdom, and the United States. I'm going to start by diving a little into the results from the United States, and then I'll generalize globally a bit after that. To dig into these results yourself in their full detail, you can check out the link we have in the show notes. You can scroll all the way to the bottom of the US results that we have in the show notes, and from there, you can access links to all of the other countries' reports.
Alright, so 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 U.S. 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, natural language processing, and PyTorch are the most common skills. It also shows that the top cities for AI engineers are San Francisco, New York, and Boston.
and that the most common roles current AI engineers transition from are full-stack engineers, 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 U.S. are fully remote 36% of the time and hybrid 27% of the time, suggesting that only about a third of AI engineers in the U.S. are expected in the office every day. Now.
If AI engineering doesn't sound like a fast growing job that you'd be interested in, I have good news for you because the second fastest growing job in the U.S. is also highly relevant to a lot of this podcast listenership because the second fastest growing job in the U.S. 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 associates. So while some AI consultants would no doubt be as technical as AI engineers, there's also room in the AI consultant's tent for folks who are more commercially oriented, operations oriented, management oriented, and or product oriented out there.
The next chunk of jobs in the U.S. amongst the fastest growing jobs aren't obviously relevant to our listenership with job titles like security guard, event coordinator, and physical therapist. But scrolling down to number 12, we find AI researcher, which is squarely relevant to this podcast audience. So that's pretty crazy. Amongst the fastest growing jobs in the U.S., you've got a number one, AI engineer, a number two, AI consultant, and a number 12, AI researcher.
AI researchers are concerned with advancing AI algorithms themselves, and so might often be even more technical, more specialized, or academic than an AI engineer is, and perhaps they might 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 number one fastest growing role in the UK and in 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. In both those countries, it's even more popular than AI engineering itself, despite sounding relatively niche to me. And yeah, AI researcher is also the ninth fastest growing role amongst the Dutch. So it's kind of a general thought. It's interesting to me that 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, clearly Data Scientist has 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, according to these LinkedIn reports. Data science skills aren't any less important than five years ago or 10 years ago, but as AI proliferates, we're seeing more and more specialized subtypes of data scientists emerge. That's exactly what this report is showing. If you're interested in learning more about AI engineering, the fastest growing job,
in many countries now, including in the US, I highly recommend checking out episode number 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 in that episode. And he also fills you in on how you can hone AI engineering skills. Alternatively, if you're more into books, you can check out the outstanding author Chip Hu Yen's brand new book, which is aptly titled AI Engineering.
We've got a link to that for you in the show notes as well. All right, that's it for today's episode. If you enjoyed it or know someone who might, consider sharing this episode with them. Leave a review of the show on your favorite podcasting platform. Tag me in a LinkedIn or Twitter post with your thoughts. And if you aren't already, be sure to subscribe to the show. Most importantly, however, I hope you'll just keep on listening. Until next time, keep on rocking it out there. And I'm looking forward to enjoying another round of the Super Data Science Podcast with you very soon.