scoobitydoobap

In line with this, Product and Customer Success folks who fundamentally don't know how customers are using the product/what their specific use cases are. Really makes further development/problem solving a challenge :)

Don't they, though? They're the sole person who's been retained to maintain this product. Seems like management have worked themselves into a single point of failure, which in my experience has meant leverage for the individual.

... but using transportation which requires little to no mental exertion or fear of getting into a violent car wreck.

Yes, unless that PhD puts you on an earnings track where you can quickly make up for a few lost years of wages and saving. Otherwise, the compounding effect of having missed investing a few hundred thousand dollars _at a young age_ is substantial.

Carbon steel is similar to cast iron in that it gets "seasoned" over time, but is much lighter (and heats up more quickly and evenly).

This sums up my experience exactly. I technically work in ML and do build (fairly basic, compared to things like ChatGPT) ML models, but the engineering – specifically as it relates to _data_ engineering – is much more interesting to me.

Additionally, the whole process of building a product around models is, again, much more interesting to me than figuring out the best algorithm to use.

Economics & stats background. Bachelor's only.

Is the alternative of them verifying your identify by sending a code to your phone or you telling them to call you at your phone number better (referring to SIM swapping)? Which commonly used verification method is the lesser of all evils?

Could you elaborate on what kind of "software engineering" you now do? For someone who also would like to get out of data science, mentions of "I became a software engineer" don't really help to clarify what kind of SWE is feasible for a data scientist with decent programming chops to get into.