People on this forum seem to be pretty pessimistic.
PharmD, MPH will get you to an associate director / director level position. PharmD PhD should be enough to get you to senior director or VP, but at that point it is based on years of experience and who you know.
And wow, the math requirements being stated here are intense! Transcendental calculus? For epidemiology? Really? Not necessary even for machine learning and AI, but do brush up on your linear algebra, probability theory, multivariable calculus and go heavy on the statistics.
This may come off as an
ad hominem argument, but you basically illustrate my pessimism.
Um, do you know what transcendental functions are? Let's be informal. A transcendental is a function that you cannot define using algebra alone as it becomes an infinite sequence. That's all power, logarithm (and e), trig, and most continuous probability functions. You ever wonder why you get "function fails to converge" errors in your code? That analytic (in the calculus definition) solutions don't necessarily exist (or are worth calculating) that force one of the numerical integration algorithms to show up? You have to go out of your way to avoid Taylor or Runge-Kutta methods that depend on transcendental convergence. While you do not necessarily need multivariate (who actually calculates or even conceptualizes multivariate given the numerous computer algebra systems out there?), you do need a good handle on transcendentals to have a chance of actually understanding the rest of what you wrote (and I agree on those topics though most do not actually take them seriously enough).
Look, superficial knowledge in computer science is analogous to being a retail pharmacist. You did the time in school, you supposedly know a little something, and you supposedly bring some value. But what value proposition is based on is something that is not easily differentiable, and like many things, fads like opening pharmacies come and go. Computer science has this effect, I saw the exact same behavior from Java programmers that I see from Python/R programmers today. These fads build platforms fast, their jobs are plentiful, and things seem fine.
But you don't see Java programmers anymore in the same way that superficial programmers learn one stack, but don't learn the why, and then when the technology or fad changes, they fail to adapt, and then they become the stereotype grey-haired unemployed programmer. I am not saying that all of these people are going to be unemployed, but the difference between a superficial and a real programmer is the ability to understand the fundamental concepts of the field such that they can adapt and move readily to the changing times.
For retail pharmacy, that adaptability just isn't there for the majority of pharmacists. I expect them to be abbatoir glue someday, even when the times were great in 2004. For hospital, it's not easy either, but those pharmacists usually work enough areas to be more flexible. In industry, especially IT, I don't accept anyone's competency until they survive at least one downturn. Being threatened by unemployment (or being pink slipped) in a time where jobs are more competitive, that's when you see the real stars show up, because they survive through the eras. We're in another IT bubble, there's only so much that can be done with that stack, but the real differentiators are those who can work without the manuals, who can figure out and trailblaze as well as keep up with the changing tech that will survive. That gets exhausting, and to be able to keep selling yourself when the tides change and reinvent yourself to the evolving market is what keeps you alive. But, you're not going to do it with only superficial knowledge of your trade. You're going to have to go deep, but also, know when to quit.
