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After that, the interviewers asked the questions about how to develop a news feed ranking algorithm at a super vague, high level. I spoke about how you would need to account for seasonality or cyclic effects -- e.g. election news would clearly be ranked higher in newsworthiness when approaching important elections, or the same idea for sports-related articles, etc. I talked about trying to determine which news articles are the most clicked-on by peers in your network, possibly using some centrality measures to rate certain users more as "trend setters" and use their news viewing habits as guides for what to show to others. I was actually fairly detailed in my description of some math ideas for these things, but the whole conversation sounded like someone sucked the air out of it. The interviewers kept trying to get me to talk more specifically about aspects of the newsfeed that actually occur in Facebook's newsfeed. When I told them I don't have a Facebook account and don't know what the modern version of the newsfeed does (which is true, I don't), they sounded like they just lost all interest in talking to me. My hunch was that they wanted me to possibly talk about A/B testing, but this is kind of silly. They definitely didn't ask me about A/B testing. It was like they wanted me to guess that phrase. Plus, at least for frequentist A/B testing, there are good reasons to believe a lot of it is junk science and a lot of applied mathematicians would not consider this a reasonable idea at all. I guess I could have talked a little about Bayesian A/B testing, but my mind associated their problem with network modeling and seasonality effect modeling, not A/B testing. (I can't be sure they were focused on A/B testing -- it's just what I independently thought after the fact when struggling to understand the outcome since no one gave any feedback). Overall, I could tell it went badly, but I could not understand what the two interviewers were trying to get me to say. As someone who has worked on a lot of math modeling problems, from undergrad and on through grad school, I thought my way of breaking down and describing ideas for the newsfeed example was perfectly on-point and technically useful. To boot, the job I was interviewing for did not (at least as it was explained to me) have anything to do with ranking problems, A/B testing, or newsfeed-like work at all. That was on 8/21/15. I then heard no response of any kind, until 8/28/15 when I got a form letter rejection email that simply said at that point, Asana did not want to continue the process, and gave no further details. From start to finish it went from 8/4 to 8/28, so almost a month. I scrambled to put in a lot of hours to solve the take-home test in my spare time over a short window, and then went through two different full week long periods of silence (one after my code test was being reviewed, and one after the vague/weird two-on-one phone interview), only to receive no feed back at all on my software solution, limited and tangential feedback on my actual data science approach (feedback which didn't seem at all appropriate for something a busy adult would put together over a couple of days), and no feedback at all about the decision to reject me based on the interviews. The overall experience left me feeling like Asana asks very vague questions and reserves the right to be dissatisfied if you don't identify the magic answer to the vague question. It also made me feel that Asana does not act in a timely manner to review code submissions or to provide interview feedback, even though Asana did require me (despite being busy outside of interviews) to complete a code test in a timely manner. It also sounded like Asana might have some data science employees who act a bit like "gatekeepers" in the interview process -- they felt a need to assert that they are better than the candidate, even if it means bringing some discussion up about specific accuracy criteria or something when it may not really be very appropriate for a quick solution to a poorly specified problem. It's a bit like someone talking about Haskell programming for an interview, successfully solving some Monad problems in a quick way given the time constraints, and then getting grilled about unification algorithms in the Hindley-Milner type system. It's just to let the existing employee who is conducting the interview feel superior and act like it is some great filter on the incoming talent pool, when really it's mostly unrelated minutiae. As I mentioned in the other comment, I later learned the extent to which Asana supports Agile-focused systems for enterprise project management, and this actually saddened me a lot. I always liked Asana because it embodied a type of minimalist efficiency that is fundamentally incompatible with the bureaucratic wastefulness of Agile/Scrum/Kanban/"lean" buzzword systems. I had high hopes that Asana would not be just another one of those cargo cult management junk systems, and maybe it still will avoid that fate, but agreeing to support Agile doesn't bode well. So ultimately it was a good thing I was rejected, though I do not feel it was a fair reflection of my engineering effort on the exam, and I certainly don't feel that the evaluation was done in a way that showed respect for the amount of time and effort that I took to complete the task. |
Re: the questions themselves, although I don't agree with your characterization of what's going on or what we're looking for, I definitely think there's a lot of room to improve on our data-science screening. I'll send you an email with more re: what specifically came up in your interview that made us reject.
Again, thanks for taking the time to share this really explicit feedback.
(For the record, I don't really know what you're referring to re: Asana and Agile.)