By VICTOR ANJOS
At 1000ml we literally get people ready for the working world; with an absolute penchant for the world of Data Science, Machine Learning and Artificial Intelligence. Fortunately, our model works really well because we are generally so much better at this than anyone else, including Universities, MOOCS, other vocational trainers, and especially bootcamps.
As such, we see every single data or data adjacent job in the GTA (and most of Canada’s). In fact, not only do we see them, we catalogue them all on our own personal job site; which is only looking for All Jobs Data (intentionally incorrect grammar, much like our podcast). Being extremely adept at all data practices ourselves, we of course took to the notebook and used our data superpowers to understand the landscape, examine the good hiring practices and of course, chuckle at the follies that Canadian (and more specifically the GTA) companies put out there as job posts. We then compared this data to North America more broadly (simply because the US is still a juggernaut in Capitalism and a huge player in the world of AI) to see how we measure up.
Because we are who we are, we used our own internal platforms (a resume checking NLP framework we created – AIDitor), which plugs into our own ATS (AITS). Yes, we know we’re not creative with names, but they’re internal, so it doesn’t matter. We were not trying to publish or even get into arxiv with this analysis, in fact, we used some of our trainees and Residents to do most of the analysis itself, so we ended up producing a few things, but this table is probably at the crux of it all.
Some things that jumped out at us (that are not specifically in those graphs and tables):
1. There is still a very large penchant for copy and pasting job descriptions
a) This often happens when hiring managers or HR staff are out of their depth and don’t know exactly who and how to hire for the data world
b) Most copies of job descriptions (or parts of job descriptions) came from incredibly sophisticated companies (the originals)
c) No company is immune to this copy/paste, we’ve seen startups, scale-ups, medium, large and Enterprise all do it.
2. Maybe due to #1 above, higher degrees are still and overly dominant ask (i.e. Masters or PhDs)
3. Way too many companies try to hire “Data Scientists” when in reality they really need any of:
a) Data Analysts
b) Data Engineers
c) Machine Learning Engineers
d) NLP Engineers
e) Data Ops or DevOps
- Most companies (smaller ones especially) do not need AI Researchers or often times even heavy modelers, they need people to make things work
- 4. Toronto and Vancouver pay the most and have the most jobs in AI, Montreal is a distant third place
5. We still index heavily towards research rather than commercialization.
There are obviously some wonderful groups, companies and agencies doing great work in this space.
1000ml itself is one such company. We very deliberately called it 1000ml for the simplicity of; we want to put a real dent in the chasm of job-ready talent in Canada, specifically in the world of machine learning. We are not after transactions like so many of our contemporaries and we don’t ever put out courses that are not all substance (and strictly project based).
Insofar, we have just developed a new methodology whereby we On-board staff for companies by making them truly professional and teaching them ONLY the things that are really necessary for that specific workplace. Imagine for a second that you are a technology company who works heavily in telematics and the technology stack you utilize is comprised of many different types of sensors which all report back through Geotab. You likely do a lot of work in .Net but want your data analytics team to focus much more on Linux, docker, python, Jupyter and Keras. Those are the very specific tools we would use to teach through projects; with the intent being that the incumbent hire then becomes context aware and much more job ready.
What does this do? It frees up your extremely valuable and productive senior staff such that they can keep your business moving at break-neck speed, while you receive a new hire who is much more apt to be usable in the short (and long) term.
What happens when you do find yourself in a great job in a data practice? You hone your skills, you keep learning and you probably keep coming back to 1000ml for more upskilling and re-training. As a result of all of these programs, we have found some wonderfully charted information (from Glassdoor) regarding career progression (paths) that are generally available for people along with Capacity in Canada. A quick remark that this is a 4.5 year old chart, so the jobs in data have actually grown substantially since then; there just happen to not be enough people to go around.
What a great time to be in the market!
- There is definitely a caveat to be weary of in these numbers, as with any numbers though, many people have become disillusioned and are no longer looking for work or are now fully in the gig economy and not reporting as unemployed. It’s totally a new day in the world of work and Canada needs to keep investing in companies like 1000ml who are here to foster, create and grow talent and ultimately get them employed.
So what do you do when all signs point to having to go to University to gain any sort of advantage? Unfortunately it’s the current state of affairs that most employers will not hire you unless you have a degree for even junior or starting jobs. Once you have that degree, coming to a Last Mile Finishing School, with 1000ml being the only one worldwide, is the only way forward to gaining the practical knowledge and experience that will jumpstart your career.
Check out our next dates below for our upcoming courses, we’d love to have you there.