This is so snobbish. What here is even think about ?
The startups and Googles of this world are the exception when it comes to employer tech-savviness.
Second, programmers at companies almost never work in isolation from other programmers, and in most open office environments are close enough they could touch another one from their desk. On the other hand, a startup with five to ten engineers may be hiring you as their first data scientist, and bigger companies may be putting you on an embedded team, with the nearest data scientist a hallway or floor away. And this isn't a big deal in terms of teamwork, but most data scientists don't have a PhD in mathematics, so if you find higher level mathematical ideas and notation to be the most efficient way for you to think about a problem, your colleagues may not. That's also not a math specific thing though -- an economist, statistician, mathematician, computer scientist, physicist, etc. are all going to have slightly different ways they think about things internally.
When you interface with non-specialists, you can't rely on the shared specialized vocabulary. This means you have to do a lot of work to distill which insights are key and figure out how to communicate them (along with supporting ideas) to an audience whose highest insight resolution is going to be varying and who may each bring their own language to the table.
So on top of building your problem domain model, you're going to have to build a model of how the people you're working with can understand it. And you will have to do this over and over.
You can be snobby about that, and say that it's soooo hard being so much smarter in your specialized field than non-specialists are, but you don't have to be snobby to realize that this is a dimension of the work that you might not enjoy or may even not be cut out for.
And of course, you don't have to be a mathematician to have experienced this. It's certainly sufficient, but not necessary. If you've been a developer in a company that has non-dev coworkers and you've never hit this turbulent boundary, you've either been remarkably well insulated, or you're so remarkably natural at that kind of job that you definitely should be doing it. :)
His audience is academics - who up until this point have been mostly in and around other mathematicians. Its important to ask yourself, "are you OK with having to sell/explain your work to people who aren't versed in even the basics of what you do?"
Obviously that isn't all data science jobs, but in my experience most of my colleagues have to evangelize their work within their organization to some level.