Amazon data science interview questions • Hacker News Board

Re: Amazon data science interview questions

#91

Post by nerdponx » Mon, Aug 21, 2017, 1:30 AM UTC

kafkaesq wrote: post #90

nerdponx wrote: post #59

Earlier 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 .

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 alre…

There is a lot of cynicism about these types of questions in this thread, but I suspect a lot of it is coming from programmers and not data scientists. As a DS candidate I always find these kinds of interview questions a valuable learning experience and a positive challenge. It also provides me some insight into the kinds of problems they're interested in, and the kinds of approaches they use.

When I mean "right approach", I mean that there are several possible approaches, but only one of them is doable in a few hours in the afternoons, where as the others might take a research team and several weeks. Being able to recognize the right approach, and to ask the right clarifying questions, is important for data scientists, in the same way that being able to choose the correct algorithm or data structure is important for (certain types of) software engineers.

Re: Amazon data science interview questions

#92

Post by nerdponx » Mon, Aug 21, 2017, 3:27 AM UTC

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…

This is data science, not software engineering.

Re: Amazon data science interview questions

#93

Post by icelancer » Mon, Aug 21, 2017, 7:43 AM UTC

botnik wrote: post #86

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…

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 talen…

>Physicists and Mathematics majors can only be better than a CS grad, if they come prepped with Softwrae Development experience

This is very much the opposite of my experience.

Re: Amazon data science interview questions

#94

Post by nerdponx » Mon, Aug 21, 2017, 1:18 PM UTC

dapreja wrote: post #74

ImSkeptical wrote: post #66

Earlier quoted context omitted.

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…

The reality is, unless you're directly communicating with someone who understands the problem/question, they'll be expecting the textbook definition as is or whatever stackoverflow response has.

In data science interviewing, that generally is the case. Technical people are going to be asking these questions and evaluating you.

Re: Amazon data science interview questions

#95

Post by kafkaesq » Mon, Aug 21, 2017, 5:32 PM UTC

nerdponx wrote: post #91

kafkaesq wrote: post #90

Earlier quoted context omitted.

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 alre…

There is a lot of cynicism about these types of questions in this thread, but I suspect a lot of it is coming from programmers and not data scientists. As a DS candidate I always find these kinds of interview questions a valuable learning experience and a positive challenge. It also provides me some insight into the kinds of problems they're interested in, and the kinds of approaches they use. When I mean "right appr…

Agreed - these questions are definitely more appropriate for research oriented positions. And actually, way more interesting in their own right than most programmer interview questions.

What rubs me the wrong way is when, applying for more or less "standard dev" jobs, one gets asked research-grade questions (i.e. "Solve this cute problem I was fed the solution to recently, either in grad school or in some handout my manager gave me just now. But which was actually an open problem in the literature for 5-10 years before a reasonable solution came down the pipe.")

For a job that ends up being, "Congratulations on making it through our interview process! Now would you mind debugging this {{horribly written X}} that no on here wants to touch with a 10-foot pole? Everyone here has to do 9-12 months of shitwork before moving on to anything interesting here, you see."