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Amazon data science interview questions

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Re: Amazon data science interview questions

#81

Post by alexcnwy »

soVeryTired wrote: post #43

My quick and dirty answers are below. I'm thinking of moving jobs within the next year so I could use the practice. Can anyone do better? > How do you treat colinearity? Throw away the redundant part of the data > How will you deal with unbalanced data where the ratio of negative and positive is huge? This is very problem-dependent, but it's got the potential to wreak havoc with your learning algorithms. You might ge…

Do data augmentation for the geographic question - overlay the lat-lons with other spatial data (e.g. demographics, distance to nearest mall, etc.)

Re: Amazon data science interview questions

#82

Post by derping69 »

beebmam wrote: post #68

As an interviewer, I don't ask questions like these. By a very large margin, for most of the time you are actively developing as a full time employee at a large tech company, you're working on integrating systems, either through a build language or extending your software to use an already existing API. What are the most useful skills for these tasks? Being able to communicate effectively, feeling comfortable asking…

>What are the most useful skills for these tasks? Being able to communicate effectively, feeling comfortable asking questions, feeling comfortable admitting when you don't understand something, and being kind and friendly with those you interact with.

I've never liked this idea or thought it was true. At the end of the day it doesn't matter how could of communicators people are if they don't have the skill or talent.

Give me a genius programmer on the spectrum over someone who smooth-talked their way through the interview but doesn't know shit any day.

Re: Amazon data science interview questions

#83

Post by derping69 »

RA_Fisher wrote: post #47
icelancer wrote: post #40

Earlier quoted context omitted.

When I ran a Data Science team we primarily hired Physicists and Mathematics majors and not CS graduates for this reason. It mostly worked as intended (untapped source of excellent candidates), but some of them could not for the life of them pick up software development / writing manageable code with a team. They were so used to writing unmanageable scripts that didn't have long-term time horizons (think: horrors of…

Yeah, my experience is almost a perfect match. These days I'm wondering how a statistician can best capitalize long-term in tech?

learn AI, start a company with a bunch of buddies with the sole intent of being acquired for your talents.

There was a company that did this a while back, I don't even think they actually had a product, they just knew that one of the big four would pay millions to acquihire them.

Re: Amazon data science interview questions

#84

Post by Xcelerate »

gaius wrote: post #76
Xcelerate wrote: post #62

Earlier quoted context omitted.

Agreed. I have horrible short term memory but these questions are trivial as a "homework" type assignment. Plus, for my current data scientist role, the interview consisted of me bringing in a project I had worked on (actual code) and demonstrating how it worked. I much prefer that tactic. I think it gives you greater insight into the candidate.

bringing in a project I had worked on (actual code I don't think commercial confidentiality makes that a viable generic method...

Should have clarirified. This was right out of grad school—so code for my research.

Re: Amazon data science interview questions

#85

Post by user5994461 »

RA_Fisher wrote: post #24
prestonh wrote: post #5

Can't say I could answer all these questions, but I think they're really great and motivate me to spend more time studying statistics. With the rise in data science and machine learning I've noticed a lot of resources devoted to teaching people how to create and train models for a given problem, but less resources on how to interpret those models and on statistical inference in general.

It's quite rare to find folks in the industry with formal education in statistics. The folks that have it can run circles around everyone else, though. Data Science and Analytics and Machine Learning all use statistical theory when boiled down.

I'd say the opposite. It's quite rare to see employees without a formal education in statistics.

That being said, there are few of them and they congregate to the big tech and finance names. It can look like everyone or noone has a formal education, depending on where you personally work.

Re: Amazon data science interview questions

#86

Post by botnik »

icelancer wrote: post #40
RA_Fisher wrote: post #24

Earlier quoted context omitted.

It's quite rare to find folks in the industry with formal education in statistics. The folks that have it can run circles around everyone else, though. Data Science and Analytics and Machine Learning all use statistical theory when boiled down.

When I ran a Data Science team we primarily hired Physicists and Mathematics majors and not CS graduates for this reason. It mostly worked as intended (untapped source of excellent candidates), but some of them could not for the life of them pick up software development / writing manageable code with a team. They were so used to writing unmanageable scripts that didn't have long-term time horizons (think: horrors of…

Physicists and Mathematics majors can only be better than a CS grad, if they come prepped with Softwrae Development experience. A CS grad can communicate better with the code they write and most of the time assuming it's straight CS have knowledge and experience of ML that exceeds a Physics/Math major.

Anecdotally, I've worked in places where this is a major source of contention - The Data Scientists treated as talented individuals (who produce broken solutions which work for cherry picked data sets), and anyone else is just a monkey who maintains and fixes the broken code.

Re: Amazon data science interview questions

#87

Post by spencer_ochs »

lottin wrote: post #52
gaius wrote: post #37

Earlier quoted context omitted.

Well yes. Before the trendy buzzword, machine learning was known simply as predictive statistics.

Or simply statistical modelling. I think there are methodological differences though. To me ML seems like a massive p-value fishing operation.

Do you mind elaborating? I can't think of any methods that explicitly (or implicitly) use p-values. The only place in the industry I have seen p-values used is AB-testing, but most seem to be trying to move to a multi-armed bandit, bayesian methodology.

Re: Amazon data science interview questions

#88

Post by screature2 »

derping69 wrote: post #82
beebmam wrote: post #68

As an interviewer, I don't ask questions like these. By a very large margin, for most of the time you are actively developing as a full time employee at a large tech company, you're working on integrating systems, either through a build language or extending your software to use an already existing API. What are the most useful skills for these tasks? Being able to communicate effectively, feeling comfortable asking…

>What are the most useful skills for these tasks? Being able to communicate effectively, feeling comfortable asking questions, feeling comfortable admitting when you don't understand something, and being kind and friendly with those you interact with. I've never liked this idea or thought it was true. At the end of the day it doesn't matter how could of communicators people are if they don't have the skill or talent.…

What about the reverse? Genius programmer that's remote and can't speak the same language as you or your team?

I think either way, a completely non-technical "good communicator" or a "genius programmer" non-communicator make it difficult to get these tasks done.

Re: Amazon data science interview questions

#89

Post by screye »

govg wrote: post #67
screye wrote: post #49

Earlier quoted context omitted.

I will try. > How do you treat colinearity? Use an LASSO as a feature selector or regularizer. > How will you deal with unbalanced data where the ratio of negative and positive is huge? User F1 score instead of accuracy as a performance metric. Try using cascade classifier. > How will you decide whether a customer will buy a product today or not given the income of the customer, location where the customer lives, pro…

Regarding the PCA before SVM step - PCA would help reduce dimensionality w.r.t linear correlations, kernels generally are used to obtain non-linear maps. So wouldn't it be wrong to say doing PCA is redundant? Also, while kernel SVM optimization is dominated by number of data points, computing the value of the kernel function for individual data points might depend on the dimensionality itself, would it not? Another i…

The cost of computing similarities is definitely linear or lower, wrt. to features. But overall, the Kernel SVM will scale to the order of n^3 wrt. to the number of points. In that sense, focussing on the number of features may not make a lot of sense.

PCA isn't lossless either. We are definitely trading off some richness in the data for efficiency. When the gain in efficiency is negligible (as is the case with SVMs) the trade off doesn't look as tempting.

I am student in the area as well, so take my words with appropriate caution.

Re: Amazon data science interview questions

#90

Post by kafkaesq »

nerdponx wrote: post #59
ggggtez wrote: post #58

These questions are largely unanswerable in their current form. Like"estimate the probably of disease in a city given Nationwide has a low probability". Like, what the heck does that even mean. I could imagine a dozen answers. I can only guess they are trying to get you to think about Bayes probability an event occurs given X independent identical trial). But that's ludicrous, because on what world has anyone proved…

The point isn't to answer the question correctly . It's to show how you think through the problem. There is often a "right" way to approach the problem and a "right" way to answer the question, even if there is no right answer per se .

The point isn't to answer the question correctly. It's to show how you think through the problem.

That's what people always say. Like, as if they actually tell from 1 or 2 of these goofy whiteboarding sessions.

It seems the most they can actually glean from these sessions is, "Huh, looks like their problem-solving approach is different from mine. Plus, they come in not having the advantage of having the solution already ready-baked and laid-out for them, in whatever interviewing guide it was I found this problem. Doesn't that suck for them? Not to mention being under pressure from doing way too many, generally carelessly orchestrated interview tracks (in their non-existent spare time) while being stuck in some job they're anxious to quit. And getting distracted by me diddling with my cell phone while they're doing headstands for me. Gosh, why is it so hard to find decent candidates, these days?"

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