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Very interesting. I wonder in what type of interviews would these answers be considered ideal? Do people at OpenAI ask these sorts of questions, or is this more

by zwaps 6y ago

Very interesting. I wonder in what type of interviews would these answers be considered ideal? Do people at OpenAI ask these sorts of questions, or is this more targeted toward other industries that require data scientists but are not populated by "stats" experts? For example, consider "What is regression?". The answer given is one way to go, but if the job posting would involve causal analysis or more statistical know-how, for example, it would probably be insufficient. I would want the candidate to speak about linear projections, about sampling assumptions, about probability models underlying the process and so on. On the other hand, I could imagine that it would not be a good strategy to start to lecture about minute details of regression analysis when applying for a standard data scientist position when sitting on front of "applied data scientists" or even HR folks. Anyone has any insights?

minimaxir 6y ago
ML research positions like those at OpenAI are a completely different level than data science interviews.
smeeth 6y ago
My experience is that interviews like these are for supporting role data science jobs. E.g. company x has a product (tech or not), they have some data, and they want to hire someone to make that data useful in improving their product or selling more of it. The general data science process is that when faced with a problem, you 1) select the appropriate algorithmic tool(s) for the problem and 2) apply the tool(s) to the data. One of the challenges in data science generally is that the tools can get pretty fucking complicated. The point of theory interview questions in general is to assess the first point, whether or not a candidate has the capacity to pick the right tool for a given problem. They want to hear that you understand some of the standard tools and what sorts of problems they are good for. If you "get" the common tools, you'll likely be able to reason about the application of new/different tools for weird problems as you face them, or so the logic goes. Everything I just said was more or less objective. This is my opinion: bad companies that do not know how to hire data scientists usually do what you're describing. They ask theory questions to assess whether or not the candidate already understands the specific tools they expect them to use. Good companies tend to pay more attention to whether the candidate is capable of understanding the universe of tools in general and are less worried about their specific application area. I should note that this is less relevant when hiring consultants or "plug and play" senior people. Of course for those roles you want to know that the candidate has done something similar already and is primed for success.

by zwaps 6y ago

minimaxir 6y ago

ML research positions like those at OpenAI are a completely different level than data science interviews.

smeeth 6y ago

My experience is that interviews like these are for supporting role data science jobs. E.g. company x has a product (tech or not), they have some data, and they want to hire someone to make that data useful in improving their product or selling more of it. The general data science process is that when faced with a problem, you 1) select the appropriate algorithmic tool(s) for the problem and 2) apply the tool(s) to the data. One of the challenges in data science generally is that the tools can get pretty fucking complicated. The point of theory interview questions in general is to assess the first point, whether or not a candidate has the capacity to pick the right tool for a given problem. They want to hear that you understand some of the standard tools and what sorts of problems they are good for. If you "get" the common tools, you'll likely be able to reason about the application of new/different tools for weird problems as you face them, or so the logic goes. Everything I just said was more or less objective. This is my opinion: bad companies that do not know how to hire data scientists usually do what you're describing. They ask theory questions to assess whether or not the candidate already understands the specific tools they expect them to use. Good companies tend to pay more attention to whether the candidate is capable of understanding the universe of tools in general and are less worried about their specific application area. I should note that this is less relevant when hiring consultants or "plug and play" senior people. Of course for those roles you want to know that the candidate has done something similar already and is primed for success.