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Has anyone else begun to resent data science?
Thought this was interesting. Across 160 teams of researchers, just about all failed to make good life outcome predictions on things like GPA, evictions, layoffs, and others. Data followed 4.5k families across 15 years, with 13k features (varied over time). Haven't looked at it directly yet, but will be turning the docs and data inside out... In the meantime, authors claim this as showing the limits of ML. Oh, and it's published in PNAS, so you know there's some big publication energy there.
https://www.pnas.org/content/117/15/8398
Got messaged by a C3 . ai recruiter. Read that wlb is bad and that the interview process is absurdly long, but the Glassdoor reviews are 4.2 and can't find actual hours worked posted by anyone. How's the culture really? I'd be aiming for DS consulting, something more functional but with DS/ML concepts as my differentiator.
C3.ai, Inc.
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Seems very wish washy!
Yeah? Can you expand on that? Not the reaction i expected! This is selling hot!
Not dismissing it completely but there seems to be a serious possibility of implicit bias in such an approach and will possibly neglect a certain segment of performing population
imma gonna let you finish but I just wanna say that...
I have a very dumb question - what's ONA modelling
organizational network analysis
There's a pretty large jump to causality being made here, and a lot of explanatory gaps that can't be waved away with "they were all the same level of seniority".
Is it the connections that made them successful? Or is it their effectiveness that led to network centrality? Are they simply mediators between highly effective team members that work in a more skilled fashion? Most importantly, how is success being defined? How does this evolve over time? At day 1, what did a "effective" employee look like?
Methodology questions abound, lots of "machine learning", "Tensorflow", etc... buzzwords. This reminds me of Faception's horse shit from a few years ago.