I’m convinced that academics are afraid of people in data that are just good at what they do and are communicative about it. I have been interviewing a lot and every time I make it to the final stage I get some academic higher up who thinks because I didn’t study cognitive science at Yale and that I’m affable that I must not be good at DS (not quantitative enough) even after I pass their Python/sql tests . This has happened time after time and I’m at a loss. Screw these gate keepers 😂

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Posting as :
works at
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Basic academic quant street cred vocab:
- Hilbert space
- sigma algebra
- wavelets
- ZFC
- any random theorem from abstract algebra
- identification problem (context econometrics)

All must only be used in conversation or subject relevant context. You won't be an automatic genius, but you'll give off a competent enough first impression even to most people like you describe. Can't dodge the occasional direct math vocab test of course--then just admit you don't know and ask where to read about it.

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Happy to chat function space approximations anytime.

I somehow ended up in the acknowledgments to the Hsu book: “Fourier Series, Fourier Transforms, and Function Spaces: A Second Course in Analysis”

I suspect it was because I was a giant PITA. I had to see for myself that the DTFT converged.

There are a lot of “senior” scientists that have major chips on their shoulders from their academic training.

Avoid them if you can.

Side note: finding truly good and enabling leaders in data science is really damn hard.

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