This was a really fun list to read through. I agree with the author that knowing as much as possible about how things work, not just what things do, is extremely useful.
However, "Just know stuff" I think is a secondary requirement (although an important one) to be successful. People really struggle to "just know stuff" if they aren't interested in the subject in the first place. People who aren't interested will settle for knowing only what things do, and not dive into how they work.
I am interested in this stuff, and actually self taught (bachelors in mathematics here). I have experience with a lot of stuff on this list, not through work or academia, or because I want to make money, but through fiddling on my desktop at home. I too got the "coveted tech job" as a machine learning engineer, but I never would have if I wasn't legitimately interested in this stuff, studying for fun in my spare time. I have seen lots of people fail to progress in this field because they _don't care_, they just want a good job.
Kind of off topic, but this is actually an integral part of my interviewing process. We give candidates a simple dataset to model, and we receive their script. The performance on the hold-out set is only weighted ~20%. The candidates ability to talk about their process, about the internals of the model they used, about the feature engineering quirks to work around model limitations, about their parameter tuning scheme - these conversations reveal how much someone is actually interested in the field, and is a great indicator for whether or not they are going to be a good contributor to the team. I've had candidates who couldn't tell me _anything_ about how the models they used actually worked. PhDs included!
However, "Just know stuff" I think is a secondary requirement (although an important one) to be successful. People really struggle to "just know stuff" if they aren't interested in the subject in the first place. People who aren't interested will settle for knowing only what things do, and not dive into how they work.
I am interested in this stuff, and actually self taught (bachelors in mathematics here). I have experience with a lot of stuff on this list, not through work or academia, or because I want to make money, but through fiddling on my desktop at home. I too got the "coveted tech job" as a machine learning engineer, but I never would have if I wasn't legitimately interested in this stuff, studying for fun in my spare time. I have seen lots of people fail to progress in this field because they _don't care_, they just want a good job.
Kind of off topic, but this is actually an integral part of my interviewing process. We give candidates a simple dataset to model, and we receive their script. The performance on the hold-out set is only weighted ~20%. The candidates ability to talk about their process, about the internals of the model they used, about the feature engineering quirks to work around model limitations, about their parameter tuning scheme - these conversations reveal how much someone is actually interested in the field, and is a great indicator for whether or not they are going to be a good contributor to the team. I've had candidates who couldn't tell me _anything_ about how the models they used actually worked. PhDs included!