I would argue that beginners only -work- on neat/ready datasets and lack the appreciation or understanding that most of the time the data does not already exist in a usable form, and sometimes not at all.
The process for making model training possible, and the process for applying models in a useful way are not taught in curriculum, tutorials, and generally not addressed in other literature (e.g. academic/research pubs)
"Amateurs and beginners focus on trying to get things right. Professionals focus on making sure things aren't/don't go wrong."
I am a bit curious on what is the minimum standard? It does feel to me (an outsider that took 1 class of ML in college) that you need at least Master in ML to get intuition on the probabilistic and linear algebra theory behind ML concepts.
On the computer science side it makes sense to understand how to build a tree, not just know how to use one. In computer science you're learning how to write algorithms, not just use them. On the MLE or Machine Learning Software Engineer side, the same rule applies and knowing linear algebra as well as probability theory is helpful so you can write your own ML.
On the data science side you're rarely inventing new ML. What you want to specialize in is: 1) data mining using ML, 2) data cleaning, by knowing what kind of input ML needs, 3) feature engineering, also by knowing what kind of input ML needs, and 4) What kind of ML is ideal to choose, eg, due to the bias/variance trade off.
A lot of these boot camps, classes, and books teach the underlying structure of ML to become an MLE, though for some sort of reason they often advertise it as data science. I find this odd, because MLE pays better and is in higher demand.
On the data science side we rarely need to know these finer details. We just need to know the ML's characteristics so we know when it is the right tool for the job, similar to knowing how to use a data structure but not needing to know how to invent new data structures.
A data scientist needs to be a research specialist which is more of a phd than masters skill, so knowing the underlying math also doesn't matter as much because we know when it is necessary to research it. It's not that a data scientist can't know it, and many do know it out of hobby or classes, but it's far from mandatory. Knowing probability theory and how to digest a problem into multiple paths forward, like how to collect data, is far more valuable as a skill.
And finally, many data scientists barely know how to write code. They're a kind of analyst. I feel like the job title isn't sufficiently explained so software engineers make a lot of assumptions mixing up MLE with DS.
edit: Also, Linear Algebra isn't that bad and is an undergrad class. Probability theory is taught lightly in DS101 freshman year, but the first year of getting a masters probability theory is often taught again to a much more rigorous. This can get a bit harder, but if you understand the basics it's not bad.
So while there's a lot more competition, the majority of these don't even meet the minimum standards to get called into interviews.
If you have solid work experience to show for, then that alone should differentiate you from the the masses. Again, a lot of these people have NEVER worked with anything relevant, and can only show you a bunch of tutorials they've followed. IF lucky, they've actually taken on more exciting datasets from sources like Kaggle, and tried to come up with something.
edit: Not to mention the data - I get extremely impressed when someone actually shows a project which involves them collecting data from the wild, wrangling it, and then doing meaningful work.
Too many beginners only work on neat / ready datasets. You'd be surprised how many candidates get completely blindsided when asked to do some trivial tasks on pre-processing data.