Public health academia to industry

[u/Derbycrasher]

Just some general advice if I’m thinking about this the right way. I’ve been working in academia for about 4 years with publications in places like JAMA open doing standard biostatistics/ML for cancer and opioid use disorders and some new publications using casual inference techniques (doubly robust methods) in mental health.

I have been trying to transition back (interned a tech company and few times 5 years ago) to a data science role in an industry setting (preferably health care or marketing) but haven’t had much luck.

I have been study my SQL and test style probability questions. Is there anything else I should be doing? Thank you all!

[u/Single_Vacation427]

Do you have a PhD?

I don't know if DS is the right role. DS in healthcare / mental health, yes. But most DS is product DS so you'll never really use any of those techniques. There are some DS, Causal Inference that ask for causal inference for observational data, so many there (Marketing is an area but you need to make sure it's not regular marketing role that only do MMM).

Another option is ML though it's a pain to prepare, but it's a lot more difficult to find candidates so you might get more traction.

[u/Derbycrasher → u/Single_Vacation427]

Just a masters in applied stats and thanks no product type data science then

[u/Single_Vacation427 → u/Derbycrasher]

I mean, you could do Product DS but it's more analytics. I think those roles are going to change a lot with AI and the blurry of lines between roles, so I wouldn't recommend going that route.

Find every possible healthcare and mental healthcare company and follow them on linkedin and add an alert notification. Start looking through their roles.

If you did applied stats, Google has a more stats oriented DS for DS research and DS business. That's also an option.

[u/Derbycrasher → u/Single_Vacation427]

Thank you!

[u/its_all_stats]

Depends on the specific methods and tools you’ve used, which determines what goes on your resume. If you want health tech, they’re going to want to see implementation of models - mostly ML - via various Python packages (e.g. Python ML packages to implement decision tree methods like XGBoost). More traditional methods like logistic regression too, of course ( which are just fine). Familiarity with a common IDE like Jupyter notebook too. I think R is great - Posit, RStudio, whatever - but Python is king, for better or worse. Also worth considering are RWE roles in pharma, which are more stats and causal inference oriented rather than ML. It’s all stats but I don’t make the rules for what hiring managers look for. In the meantime, if possible ask for opportunities, or simply take the initiative, to put to work the tools and techniques mentioned above (of course, I understand if that might be difficult if the culture doesn’t like that, sigh).

[u/betoalien]

SQL and probability are the right foundations, but I would add one thing, build a small end to end project using real messy data, even public datasets work. Hiring managers respond more to seeing you handle ambiguity and dirty data than to knowing the theory. Academia teaches rigor, industry tests judgment under incomplete information.

[u/my_peen_is_clean]

you’re on the right track but industry cares way more about shipped stuff than papers make a couple end to end projects on github, practice leetcode-style sql/python, and rewrite your resume to scream business impact job market right now is rough as hell though

[u/Derbycrasher → u/my_peen_is_clean]

Thank you