As the title suggests, I am a statistical modeler. To be exact, I have a PhD in economics/applied econometrics, and have been professionally trained as a statistician. I work in consulting now for pharma and biomedical companies. This means I regularly run regression models (linear and GLM), longitudinal/clustered models (GEE/GLMM), propensity score modeling to approximate causal inference. I use bootstrap and Bayesian inference pretty regularly. I know a lot about randomized controlled trials which are similar in design (if not implementation) to the A/B experiments used in the tech space. I run a lot of computational experiments (eg, Monte Carlo simulation) to test new methods before taking to scale across client project. I can troubleshoot, lead teams, and teach methods.

On the tech side I know R, git, various Bayesian sampling tools (eg JAGS), Stata, Mplus. Mostly statistical programming. I've learned and re-learned SQL (I don't use it often enough). I've only dabbled in Python. Obviously I have less expertise in SQL/Python, which is my limitation. But I teach people new skills all the time, and I have learned and re-learned skills before. If it means working late hours to figure out a code base at first, then I can do that.

Does this fit the profile of a data scientist? How easy is it to switch from one sector (like mine, which is in health care) to another (such as fintech, entertainment, manufacturing, or something else).

I am doing very well in terms of role and compensation, but I am tired of doing the same kinds of analyses over and over. I am not motivated anymore. I want to try something completely different. Has anyone done this? Any tips on how to kickstart this next step? Do I need to pull a Chandler Bing and take an internship at age 40?

Any and all suggestions are welcome.