Agreed. Maybe this is different in different markets but in London the vast majority of "data scientist" positions advertised are really PowerBI, Excel, etc. If there is any ML it is just to feed data into a black box model. If you were both smart and lucky you might be able to sneak R in by the backdoor and start doing actual data science, but it would be an uphill battle.
Make yourself both ML/DL master and SWEng and you'll be doing extremely well.
If you want to shine, do Deep Learning (research if possible), low-level distributed systems you have control of as an author of an important piece of infrastructure or the actual data modeling and predictions.
ETL pipeline queries and configs are absolutely critical, make or break an ML project, and account for most of its labor hours. But they are the most commoditized part of it.
And ML scientists are just SE with PhDs in ML.
Basically, they tend to try and not create seperate isolated roles in specialized domains, but instead hire people who can do and figure it all.
The opposite seems true in smaller companies. They break things up, try to hire different roles, specialist in different domains, and segregate each one from one another.
Obviously, mileage may vary, and I'm doing a massive generalisation of big and small businesses, but this is just my observation.
Startups try to hire people with double/triple disciplines to reduce costs though.
Data scientists who do ML don't have the same prestige (and pay) as those who have that ML stamp from universities.
I've always taken the ML stamp from universities as a visible sign that you're a newbie, if not purely because those qualifications haven't been around for a sufficiently long time, so its basically a signal when you've got one that you must be just starting out.
If I'm choosing to train up for job A or job B, I mean, money certainly isn't the only consideration, but it's certainly a very important consideration.
I'm not arguing that there's no distinction between the fields, just that people who excel in one can usually do a passable job working in the others. Or rather, you have to have passable skills in the other fields to excel at your chosen field.
You can spend your entire tenure explaining to people that just because something starts and serves up a web page, it's not a good idea to run 30 year old software in Production... eventually you get to the point where you can only hire Senior Software Engineers to take care of our software and then it becomes a fucking priority to unfuck your stack because your payroll costs are the most expensive line item.
Yes I'm salty as fuck.
Payroll costs are always the most expensive item. And they should be.
If you're a tech heavy company, you'll sometimes see a cloud bill being higher than your payroll costs.
Indeed.com shows a $121k average for Toronto. Does that sound somewhat accurate? I'm only making $85k so maybe I should be asking for much more.