Jon Krohn: 00:05
This is Five-Minute Friday on the fastest growing
jobs of 2024.
00:09
Welcome back to the SuperDataScience Podcast. I am
your host, Jon 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 Loncaric. 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.
00:59
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 guest’s 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 tightrope to walk.
01:33
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.
01:53
Okay, onto 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.
02:23
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 this same thing.
02:54
One approach to assessing job growth that I came across
recently that I thought was decent was done 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, and although they didn’t
disclose in their methodology, a minimum count
threshold so that some super rare job that, say, grew
from one to 100 from 2022 to 2024, that that doesn’t
show up in their results, because yeah, that job grew a
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.
03:45
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.
04:18
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.
04:39
All right, 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
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,
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.
05:16
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.
05:35 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 this second-fastest growing job in the US is also
highly relevant to a lot of this podcast listenership,
because 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 transition from being operations
associate. So 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 out there.
06:17
The next chunk of jobs in the US 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 US, you’ve got at number one
AI engineer, at number two AI consultant, and at 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.
07:19
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.
07:39
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.
07:59
So as 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 seeded 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.
08:52
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 Huyen’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.
09:31
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 SuperDataScience Podcast with you
very soon.
