Azmisov t1_istdwdb wrote on October 18, 2022 at 3:49 PM

Well, it is the top 3%. Out of 100 candidates, 3 of them are able to find a solution, do it quickly, and don't need to search the web to figure out how to do things. I understand it seems unrealistic, but just think about it competing for 3 spots out of a 100... I'd say they're justified in nitpicking, since 97% of data scientists are going to be just like you. Seems like the only thing you need to pass is some practice looking things up in the NumPy/Pandas/etc documentation, instead of relying as much on stackoverflow or plain web searching? Honestly, I know this sounds harsh, but I'd be a little worried if a candidate couldn't figure out how to filter NaNs without searching the web.

Mogady OP t1_isthauc wrote on October 18, 2022 at 4:11 PM

No man it doesn't work like that, yes you might be worried if this is the only thing you asked about, but The Nans part came late when I already used almost all the features but the last two had them and I had 5 min left, I can do that easily with Pandas but NumPy is a little complex a[~np.isnan(a).any(axis=1), :], also when you say 97% like you, what is us? let's say this month you worked with tabular data, and the next month you worked with a CV project, are you expected to remember all the syntax of openCV, Pandas, Numpy,Sklearn at that point?

iforgot120 t1_istiohb wrote on October 18, 2022 at 4:20 PM

Btw, numpy has an nan_to_num function.

Azmisov t1_istr5kb wrote on October 18, 2022 at 5:15 PM

You have 100 data scientists who all have a degree, career experience, maybe a few cool projects under their belt. If you can only pick three of them, why not pick the ones that can solve a problem faster and show a little more skill in coding? Tough, but if there's a lot of competition for a job, that's just how it goes. Also you said you were also allowed to use the documentation, which I think is pretty reasonable, so you don't have to have the entire API memorized.

drinkingsomuchcoffee t1_iswij8p wrote on October 19, 2022 at 5:26 AM

I would expect a place that hires the top 3% to design a better test.

nogear t1_iswuyiq wrote on October 19, 2022 at 8:14 AM

Probably depends on the role. When we are looking for data scientists we usually interview for excellence in the "scientific" part of it. What do I care how good a applicant memorized the awful pandas API? At least for us that doesn't make the difference. Scientific creativity and persistance as well as understanding of model requirements and good judgement is what we are looking for.