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Mr Bean ❤️

What will be the in-hand for this offer?

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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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The hands on part will definitely include SQL and Python/R. I wouldn't even discount getting a leetcode question from more techy companies, though rare enough at analyst technicality level to not study for.
You'll also get concept questions about statistics. Think regression, probability, and hypothesis testing. You should be able to talk about equations around these. Can't hurt to brush up on time series just in case. Exponential smoothing is a common exercise. Pure math questions are extremely rare, safe to ignore. In interview, don't encourage them by saying you're great at math unless you're ready to defend that with integration.
Be ready for basic ML concept questions as they're getting more common. You don't need to know techniques inside and out, but you should be able to use decision trees and know what a ROC curve is. A few shops might expect in depth knowldge, but again cut your losses--they're really looking for a data scientist or an applied scientist at that point.
Case questions are likely, brain teasers are not, but both possible. Practice estimation questions (piano tuners) and skip the esoteric ones (lying knights).
If there's a visualization piece and you're interviewing with another analyst or designer, remember to chant that pie charts are evil. Unless they use them, then shrug it off.
What stage the startup is and who is interviewing you will impact this, but generally questions will be practical and highlight your immediate ability to add value.
Brain teasers are usually ignored for technical questions like explain certain data analytics techniques or how you'd structure a data pull. Maybe you would walk through brief cases regarding how you'd do something at a granular action level rather than strategic level like in consulting.
@D1 it would appear you don't know how to read...
You better know your SQL and python