[reureu]

I totally agree, and wasn't arguing that a new title wasn't necessary. And I'm ok with my downvotes for that comment :)

It's just funny that "Data Scientist" seemed to be originally branded as the more technical/engineer-y version of a data analyst. Now I get recruiters contacting me for "Data Scientist" positions that entirely revolves around SQL and excel, and nobody in the Bay Area hires "Data Analysts" anymore.

Alright, guess it's time to update my LinkedIn and resume to adjust for this inflation? Maybe I should jump up a few inflation levels and just become a "Deep Learning Engineer."

[borroka]

I do not see any problem with that. There is a ton of confusion in the tech world regarding labels, who does what, it is needed or not, outside of the core actions that need to be done. The net effect of laying off 50% of tech people from public tech companies might even result in a net positive for the companies. Not for a tech worker like me, so please do not tell them.

Taking advantage as much as possible of hypes and other people's lazyness is fine in my book. It is certainly not my duty from the outside to educate recruiters and business people who make hiring decisions on the field – when I tried, from the inside, to gently point out that what they were thinking did not make any sense, I just put myself in a dangerous spot. I can be a data scientist, deep learning engineer, machine learning engineer, machine learning research scientist, whatever pays more and whoever has the most fun. If using an RNN instead of a more effective and efficient linear regression gives me more money and prestige, I will do it – as an IC you either go with the flow or you are not having a good time. The vast majority of us is not saving lives anyway.