Dove-tailing off of the second Academia thread above, do the dynamics regarding the importance of a University's, the PhD program's, or the thesis advisor's ranking/reputation change for a non-traditional student?
Non-traditional student example trajectory in question: industry experience -> then BS in Mathematics -> more industry experience -> MS in Mathematics -> more industry experience ~> PhD program ~> apply to industry research lab
where ~> indicates a future leg of the trajectory
The industry experience is varying data scientist positions with no research experience in school nor any publications. This involved coding for 12+ years, working on large scale systems on TB-PB scale data flows, built and evaluated ML models on a variety of data in various environments and configurations. Both the undergraduate and MS institutions were liberal arts institutions (i.e.: not top tech schools). The potential PhD school is a R1 school, but #115 on US News. The target industry labs would be commensurate with those mentioned in the second post above: Google Research, AT&T, F(M)AIR, etc.
Given the different considerations in the two posts above, I think my inquiry can be pivoted into alternative (or complementary) questions:
Does technical industry experience, at the time of entering a PhD program, help alleviate any doubts that might arise from a PhD from a low-ranked school?
- I'm guessing that the answer is "no" other than it would presumably better prepare me to be successful in a PhD program and research lab.
If technical industry experience doesn't bolster an application, would there be a significant advantage to waiting and trying for a better ranked PhD program?
Update WRT to questions and clarifications posed in the comments:
- My shorter-term goal is to establish a track record of research with a mathematical (including applied mathematics focus)
- My longer-term goal is to work in more research focused positions
- A broader goal is to learn how to do research in (applied) mathematics
I'm in industry, but most of my experience has been implementing algorithms and approaches elsewhere with some engineering in order to get it to work in an industrial setting (e.g.: data pipelines, optimizing storage mechanisms, hooking into UIs, minor adjustments to objective functions, daisy-chaining different models together). I've applied for a number research-oriented positions without success thus far. Two recurring reasons for not being selected have included that a PhD is required and/or publication track record is required. My professional positions up until now haven't supported publishing.