3 ms·

I think they're fine as long as you know the format and have an opportunity to prepare or just get in the right mindset for it. And some things (like binary sea

• disgruntledphd2 5y ago
  Facebook Product Data Science has always been a Product Analyst role more than anything else. I did the interviews a while back, and it was a pretty fun experience, but it's not what a lot of people call data science.
  • jstx1 5y ago
    > but it's not what a lot of people call data science I think that's changed a bit over time and the term has expanded to mean more things. In addition to Facebook, another great example is this article from Lyft in 2018 where they say that they're renaming all their data analysts to data scientists and all their data scientists to research scientists - https://medium.com/@chamandy/whats-in-a-name-ce42f419d16c https://medium.com/@chamandy/whats-in-a-name-ce42f419d16c
    • disgruntledphd2 5y ago
      This is called title inflation. Like the hilarious thing about Facebook and Data Science is that the term was invented there, and they needed to retitle all of their analysts (like Product Data Science) as they couldn't hire any analytical people with an analyst title in SV (or so I have been told). Like, data science was defined back in the days as a social science PhD who could run experiments and write MapReduce jobs. I'm pretty sure that most people would disagree with this definition these days.
      • jstx1 5y ago
        Yes. My point is that the person running experiments and mapreduce jobs is still called a data scientist. But so is the product analytics person (btw product data scientists run experiments too). And there are some other data scientist job profiles too (more focus on research, more focus on engineering etc). So it's not really a complete redefinition of the term, it's more of an expansion of the types of jobs it covers.
  • kevinventullo 5y ago
    In my experience, it varied greatly from team to team.
• minimaxir 5y ago
  > And some things (like binary search) should be easy to write anyway. It's a different story when a) your mind is set on statistics/linear algebra b) you've never had to actually implement binary search by hand since college and c) even if you do implement the algorithm and demonstrate that you have a general understanding, it must work perfectly and pass test cases otherwise it doesn't count. FWIW I was rarely asked about algorithmic complexity which is more relevant in DS/ML, albeit it's usually in the context of whiteboarding another algorithm and the interviewer mocking me for doing it in O(n) instead of O(logn).
  • kragen 5y ago
    Binary search in particular is surprisingly tricky, which is precisely what makes it useful for telling if someone knows how to program. To a significant extent, though, you can cheat by studying binary search itself, which is a surprisingly beautiful thing. I like this formulation for finding the first index in a half-open range where p is true, assuming p stays true thereafter: bsearch p i j := i if i == j else bsearch p i m if p m else bsearch p (m + 1) j where m := i + (j - i)//2 Or in Python: def bsearch(p, i, j): m = i + (j - i) // 2 return (i if i == j else bsearch(p, i, m) if p(m) else bsearch(p, m+1, j)) The only tricky thing about this formulation is that m < j if i < j, thus the asymmetric +1 in only one case to ensure progress. If invoked with a p such as a[m] >= k it gives the usual binary search on an array without early termination. The i + (j - i) // 2 formulation is not needed in modern Python, but historically an overflowing (i + j) // 2 was a bug in lots of binary search library functions, notably in Java and C. (Correction: I said a[m] <= k. This formulation is less tricky than the usual ones, but it's still tricky!)
    • minimaxir 5y ago
      > Binary search in particular is surprisingly tricky, which is precisely what makes it useful for telling if someone knows how to program. That's the problem. There are many other ways to do that without risking false negatives and annoying potential candidates (e.g. I would not reapply to places that have rejected me due to skepticism about my programming abilities and using tests blatantly irrelevant to day-to-day work because it's a bad indication of the engineering culture). Even FizzBuzz is better at accomplishing that task.