My interview was outrageously more math-y than this. Sadly, Amazon NDAs these things pretty hard.
Amazon data science interview questions
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Re: Amazon data science interview questions
#62I have mixed feelings concerning this methodology. At the same time I feel I'm a bit rust on the details of the field, I still believe best way to assess academic background is by sending a paper "offline" and asking the interviewee to explain it. IMO all these quick-answer questions asses is how prepared is the interviewee in answering quick-answer questions.
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.
Re: Amazon data science interview questions
#63These 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 .
I've never understood how there is a "right way to think about something". Isn't it an attribute of humanity that we all think and approach problems differently? Are companies really looking for one single type of individual to clone across all their engineering teams?
Re: Amazon data science interview questions
#64These 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 .
How I so desperately wish that were true in the general case. Sadly with many large corporations there is one single answer they're looking for and damn the false negatives.
Re: Amazon data science interview questions
#65Earlier quoted context omitted.
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 .
I've never understood how there is a "right way to think about something". Isn't it an attribute of humanity that we all think and approach problems differently ? Are companies really looking for one single type of individual to clone across all their engineering teams?
[deleted]
Re: Amazon data science interview questions
#66Earlier quoted context omitted.
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 .
I've never understood how there is a "right way to think about something". Isn't it an attribute of humanity that we all think and approach problems differently ? Are companies really looking for one single type of individual to clone across all their engineering teams?
I believe it's not that there is a single right way to think about a problem, but there probably are wrong ways to think about the problem.
For example, going through all the issues with the question and implicit underlying assumptions, talking about what you'd need to give a good answer, or the characteristics a good answer may have might not be exactly what the interviewer expected or was looking for - but such an answer could demonstrate your competence in the field and be a good answer.
On the other hand, trying to make up an answer, or brazen your way through with jargon while hoping the interviewer didn't notice would likely be a bad answer.
It's not that these companies are looking for a single kind of thought. On the contrary, I believe they value different kinds of thought. Instead, they believe they can sort kinds of thought into desirable and not.
Re: Amazon data science interview questions
#67My 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…
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 instance of lower dimensional representation possibly helping.
Please correct me if I'm wrong, I'm still just learning this stuff.
Re: Amazon data science interview questions
#68As 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 questions, feeling comfortable admitting when you don't understand something, and being kind and friendly with those you interact with.
Therefore, when interviewing new hires, I do not ask many technical questions beyond a general competency question.
Re: Amazon data science interview questions
#69These 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…
[deleted]
Re: Amazon data science interview questions
#70Earlier quoted context omitted.
I've never understood how there is a "right way to think about something". Isn't it an attribute of humanity that we all think and approach problems differently ? Are companies really looking for one single type of individual to clone across all their engineering teams?
I believe it's not that there is a single right way to think about a problem, but there probably are wrong ways to think about the problem. For example, going through all the issues with the question and implicit underlying assumptions, talking about what you'd need to give a good answer, or the characteristics a good answer may have might not be exactly what the interviewer expected or was looking for - but such an…
This is exactly it. Thanks for laying it out clearly.