As someone who just finished a ph.d. in (non-applied) math I would agree profit incentives are a large percentage of the reason - in programming, you can learn enough basic skills to make money, and then smoothly transition that to higher skill / profit levels just through the experience you gain doing more actual programming. In math, you can't really make money with a lower level of math skill (your options of making money at a lower math level are maybe accounting or teaching, and neither of these provides a transition to deeper math skill levels). Thus your option to increase your math skill is to tough it out through 5 years of grad school making about minimum wage, and then another three years of a postdoc, making a 1/3-1/2 of the average software developer salary, and then you can start making more money if you are lucky enough in the problems you choose, and work hard enough.

I would guess the more applied math you care about / the closer to software your math is, the better you can do as a self-taught. I actually know of one guy who transitioned from an engineering ph.d. to teaching applied math doing self-taught / working on fluid dynamics in aerospace, but I expect the examples of someone doing this in more abstract / pure areas are extremely few.

Just chiming in to add another vote that this is the explanation. In most business settings, business types don't like mathematical maturity or really even care unless it is fundamental to the bottom line -- and even then, it's usually only fundamental to one tiny corner of the product or business, and after that everything else is reporting software, client analytics, customer support, websites, etc.

Businesses interface socially with other entities (individuals, governments, corporate customers). That social interface will always be 99% report/client service/cursory analytics and 1% hard science domain expertise. That's just the nature of business.

It could be different in a government lab, a think tank, or a boutique consultancy, and it's definitely different in organizations like prop trading firms that do not interface with outside entities in the way that almost all businesses do. But all of those things also place a much higher value on employing people with significant math maturity and hard science expertise.

I think this is it. To be good at mathematics you need a considerable amount of time to sit and do mathematics. The only way someone is going to fund this as part of a job is if you're already a mathematician.