> What routine tasks and skills of yours have become so second nature, you sparsely think about them anymore?
Noticing hypotheticals. I think this is similar to being paranoid about possible bugs in your code. In the same vein when you do statistics and obtain some result (i.e. something between group A and group B is different by x%) you start automatically imagining scenarios that could have caused this result as a false-positive. Maybe the groups were somehow imbalanced, maybe the methodology used did something differently for both of those groups, maybe there was a sampling bias.
> As a careered data scientist, what separates industry practice from academia? What project, or task helped you realize this?
In industry your result is typically subservient to some other purpose (i.e. making decisions, increasing sales, reducing costs, predicting customer loyalty). In academia the result is typically at the forefront (i.e. an observation worth writing a paper about)
> Did you wear rose color glasses before practicing in your field? What cleared them?
No, I started working with statistics before the term "data scientist" became popular. I did have some hopes after the articles with "statistics will be the sexiest job of the 21st century" appeared. But it quickly waned. The hype brought a lot of noise to the field and the focus shifted from what (in my opinion) is most important - working with open eyes. Today a lot of "data scientists" don't have fundamentals in statistics, use methods they don't understand, focus on secondary things like programming languages and tools, and think of themselves as having an "imposter's syndrome".
(this part turned out to be quite ranty but I want to leave it like that).
> What is the monotony or grunge work of a data scientist?
Staring at a computer. Preparing the data, cleaning the data, combining the data, looking for more data, presenting your results.
> What is the kind of work that makes it all worth it?
Getting insights and intuitions that other people don't have. Being the first to discover something novel, interesting, or valuable. Reflecting back on what you do in a more general way, i.e. asking questions "what it means to know something", "can data really speak for itself", "does every kind of analysis require having some initial assumptions". And if you are good then you can get to work in a variety of different areas, from predicting the results of an election, to analysing 3d scans of brain patterns in schizophrenia.
> What other questions should be seeking answers that would help me understand what life as a data scientist is really like?
Talk is cheap. If I were you I would try to experience it myself. Volunteer somewhere or start participating in some open competition (like maybe Kaggle) and see how your day will look like.