111–120 of 175 posts
Bit of a silly article. You could say this about anything. And I don't see what the problem is with never being contacted, if someone doesn't want me for whatever reason, I don't really want to hear from them again.
At least you have been told the reasons, even if they are true or not. I recently had many tech/behavioral interview with team and passed, but after a review with VP of X, saying we decided not to move forward is way worse than this. I still keeep wondering 'What is wrong with me?' even after years of interviews. I have some suspicions, but never a promising answer. If I oneday found a company, first company policy w…
apply for 100 jobs, get zero response, and zero reason why.
be told illogical things.
be told out right lies.
welcome to capitalism. welcome to the workplace that is run entirely by data. (or by people who only care about data). welcome to the future of humanity.
I manage a data science team and revamped the hiring process pretty substantially about a year ago, to good results. Nothing in here is particularly original, but here's what we do: 1. Break down "data science" into several different roles–in our case, Analyst (business-oriented), Scientist (stats-heavy), Engineer (software-heavy). Turns out that what we mostly want are Engineers-Analysts, so our process screens heav…
Could you talk more about your phone screens? What criteria are you filtering on? What sorts of questions have you found to be effective at stratifying candidates?
An engineer who wants to learn data science is a great fit for us, an academic who wants to write R all day is not (though an academic who wants to learn engineering/functional programming is fine!)
Beyond that, I ask some questions about projects they've worked on, and in particular, how their approach would change if assumptions were different. Here I'm looking for the ability to reason backwards from a business goal, as opposed to somewhat blindly applying statistical techniques.
If they do well on these, we send the take-home exam. As previously noted, this is specifically designed to require relatively little knowledge but heavily test analysis skills, and lightly test programming skills. It's almost impossible to complete this exam without using Google effectively, so that's another thing I'm testing.
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I've probably interviewed about 70-100 such people in the past year and a half. Exactly 1 such person was qualified (I hired him). The issue in my view is the following: people who know both statistics and computer science are extremely rare. People who actually understand statistics are rare. I can probably weed out 1/3 to 1/2 of candidates simply by asking what a p-value is, or what precision/recall are (this inclu…
Ignoring what a p-value is does not mean that you don't know statistics. p-tests are not some inherent statistical property, they're just a useful model for significance. People coming from a CS background most likely didn't have to deal with p-values, but they can still be good at linear algebra or bayesian statistics. (not sure I can defend somebody that does not know what precision/recall are)
I'd happily take a Bayesian answer if they preferred that, but that hasn't happened very often.
Earlier quoted context omitted.
Could you talk more about your phone screens? What criteria are you filtering on? What sorts of questions have you found to be effective at stratifying candidates?
I'm mostly looking for people who want to learn about engineering with largeish datasets (~100gb/day for us), and have some of the prerequisite skills. Our codebase is mostly in Spark/Scala and uses functional programming idioms, so I'm looking for people who either know or want to learn how to use those. I'm also specifically trying to filter out people who mostly want a stats-heavy, machine learning heavy job, sinc…
Would not python with numpy be a better fit ? or fortran with some handwave interface code
Back (early 80;s) when I did map reduce we used PL1/G
> "Quite honestly given your questions [about vacation policy] and the fact that you are considering other options, [we] may not be the best choice for you." I had a very similar experience. Job offer was basically on the table and then they balked because I mentioned that I had another offer (at a larger company, which they seemed shocked/annoyed with) and I had a question about parking at their new offices. The cur…
Earlier quoted context omitted.
I'm mostly looking for people who want to learn about engineering with largeish datasets (~100gb/day for us), and have some of the prerequisite skills. Our codebase is mostly in Spark/Scala and uses functional programming idioms, so I'm looking for people who either know or want to learn how to use those. I'm also specifically trying to filter out people who mostly want a stats-heavy, machine learning heavy job, sinc…
can I ask why functional programming in particular I can see why you might want to avoid java for big data - but isn't the average ML algo more in the procedural mould? Would not python with numpy be a better fit ? or fortran with some handwave interface code Back (early 80;s) when I did map reduce we used PL1/G
Earlier quoted context omitted.
I'm mostly looking for people who want to learn about engineering with largeish datasets (~100gb/day for us), and have some of the prerequisite skills. Our codebase is mostly in Spark/Scala and uses functional programming idioms, so I'm looking for people who either know or want to learn how to use those. I'm also specifically trying to filter out people who mostly want a stats-heavy, machine learning heavy job, sinc…
can I ask why functional programming in particular I can see why you might want to avoid java for big data - but isn't the average ML algo more in the procedural mould? Would not python with numpy be a better fit ? or fortran with some handwave interface code Back (early 80;s) when I did map reduce we used PL1/G
From a business standpoint though, there are a few main reasons:
–Data pipelines are well modeled as functions: they take a few input datasets, return a few outputs at the end, and do a ton of processing in between
–FP idioms generally make parallelization easier, and this is very important for the datasets we're dealing with
–A strong type system like Scala's lets us prevent many runtime errors, which is quite important when your pipelines can take several hours
–It's fairly trivial to wrap a statistical/ML algorithm in a pure functional interface, even if the algorithm itself is imperative
Earlier quoted context omitted.
can I ask why functional programming in particular I can see why you might want to avoid java for big data - but isn't the average ML algo more in the procedural mould? Would not python with numpy be a better fit ? or fortran with some handwave interface code Back (early 80;s) when I did map reduce we used PL1/G
The most direct reason is because the current team enjoys functional programming. From a business standpoint though, there are a few main reasons: –Data pipelines are well modeled as functions: they take a few input datasets, return a few outputs at the end, and do a ton of processing in between –FP idioms generally make parallelization easier, and this is very important for the datasets we're dealing with –A strong…