nmrcq

This is true, but these benefits are not necessarily exactly as good as they look on paper.

For example, I work in London, and I routinely have to resort to private medical healthcare because wait times at my borough NHS clinic can be 2-3 weeks, even before COVID-19 hit. In one serious incident, I was not able to be seen at either a local GP or a local 'walk-in' emergency room due to standard winter capacity issues, and so had to shell out 250 quid to see a private GP, who didn't even have the equipment to suture me up, so my friend, who works as an NHS GP, did it for me in his apartment. Dental coverage is extremely limited here too, although it does cover basic cleaning/checkup services (so it's better than Canada in that regard).

As for days PTO - sure, but it's going to depend again on the company. You may or may not be able to actually take that PTO at fear of reprisal (indirectly or directly). At least on paper they can't stop you from doing it I guess. Fair point on the maternity as far as I can tell.

At the end of the day, would I trade the above benefits (with the exception of maternity leave) for 200k USD per year? Uh - yeah, for sure.

Seeing this at the top of any piece of code running in a production environment is enough to induce an anxiety attack :(

My first thought was that this was related to multilinear algebra (algebra of tensors and higher-order vector spaces) which has lead to many interesting and suggestive advances in machine learning, but apparently it's not. What a truly opaque collection of nomenclature they've chosen...

After glancing through a couple papers I can't entirely shake the feeling that the entire thing might be a social experiment to see how much jargon and fantastical sound words can be mashed together before people notice it's nonsense (even though I realise it's not).

With all due to respect to the author of the site, mastering all the materials in the machine learning or data scientist path will make you a solid 'applied' machine learning/statistical learning practitioner, but not an expert, and definitely not a research candidate.

We should be careful with how we guard our scale of semantic meaning - if somebody with an undergrad understanding of statistics (frequentist statistics only in this map) can be called an Expert in a statistical/mathematical field, what do we even call somebody with all the same applied software engineering & exploratory analysis, and business experience, but also a PhD in theoretical topics (math, stats, etc)? A 'super expert'? What do we call Francis Chollet, or LeCun, or anybody else? What's the differentiation between an expert from the roadmap and the team of individuals deploying GPT-3 into Google Assistant? Are they the same?

As a hiring manager and team lead for a large fintech firm in London, I would happily see an individual who had really mastered the above path(s) as a strong candidate for an intermediate or upper junior role in applied data science/machine learning. But ... it's not enough to be a senior, and certainly not an expert. Just my two cents.