but its actually really typical that when something becomes an advantage for being selected to a certain pool
the success of the those in the pool after the selection will negatively correlate with that thing
the really obvious example of this is the hockey birthday thing from Malcolm Gladwell's Outliers
people with the earlier birthdays were more likely to make it past each selection stage in becoming an NHL player
but those with the later birthdays who were able to be selected in spite of their later birthdays, were typically more successful after the selection
The authors contend that the strategy might actually work against a team's success because they found that players born later in the year and drafted later actually had more productive hockey careers.
Deaner said the study showed that men drafted in the second half of the year were about twice as likely to have successful careers in the NHL ??? reaching benchmarks like 400 games played or 200 points scored ??? than those born earlier in the year.
"If the team wasn't making this mistake, they probably would have been more successful," he said. "The guys born in the first part of the year are much more likely to be busts."
Could be relevant:
Whenever I hear a conference has done this, I subconsciously deprioritize attending a talk given by a minority, since they got in with a lower bar...
Terrible I know, but I'm not sure it is illogical.
Also, for people coming from more challenging circumstances, getting the same results is literally more impressive.
Just like hockey players born earlier in the year were more likely to be drafted.
* Prior to officially selecting candidates based on performance in problems derived from programming competitions, candidates who excelled at programming competitions were likely to do well on the job.
* That correlation was observed on a wide scale by employers, so many companies -- Google chief among them -- started incorporating such questions into the official interviews.
* Candidates now observed the change in employer interviewing methods on a wide scale and adapted their preparation methods. This fundamentally changed the pool of people good at programming competition problems in such a way as to reduce the correlation between the original signal (good at algorithmic problems) and the goal (good at the job).
* Overall, widespread acknowledgment -- and all consequent changes in behavior -- of the original correlation between the signal and the goal significantly reduced the quality of the correlation.
Programming interviews and programming competitions are very similar, much more similar than programming interviews and real world software engineering. When you are selecting top programming competition competitors, you are implicitly selecting people who will absolutely smash your (non-design) programming interview questions. This has little to do with their effectiveness as software engineers.
So if you measure hiring candidates by their performance in programming competitions, everyone will manage their own skills towards doing better in competitions.
Thanks for the explanation! The idea being discussed didn't quite click for me until I read this
That is, despite passing on otherwise talented people, those people who successfully pass Google interviews go on to be successful at Google.
edit to add:
Couldn't find that original article, this article goes on to speak about success predictors: https://www.wired.com/2015/04/hire-like-google/
Because what you've stated: 'data shows' ... "successfully pass go on to be successful" - sounds like cargo cult science or pseudoscience to me.
Because it is definitely possible to do it properly. Substitute the results (or partial results) of the interview with rnd(), use it for hiring decision for a subset of candidates. Keep this information confidential. Establish if parts of your interview process don't perform better than randomness a few years down the line.
It's possible to do. Only I don't think this was done. And if it was not done, and the method was some 'data shows' with hand-waving - it would be under definition of pseudoscience.
The reason is that programming competitions give more of a boost to your odds of being selected than to how well you'll do on the job. So people who otherwise wouldn't have gotten in now will, and will not perform as well as the people that they displaced.
Which is what happened with hockey players. Being born at the right time of year put you in a bucket with people who were slightly younger than you. Which improved your performance on the tests, but didn't matter once you all grew up. So slightly worse people at the right time of year displaced slightly better at the wrong time of year, and the average came out that people who got through and were born in the latter half of the year were actually better.
However, I've noticed that they are popular in prop trading firms, where work tends to be in very small teams or individual. I don't know how their performance correlates to fund performance.
If I were hiring, I'd still prefer to hire at least some top ICPC performers. The hard algorithms are rare---but can make or break your product.
I also think the knowledge learned from programming contests is invaluable. I'd like to be able to discuss bipartite matching or min-cut with my colleagues without eliciting a blank look.
The reality with many companies these days is that the algorithmically hard problems are solved in the frameworks and libraries they use, it's simply not necessary for most engineers to understand their inner workings.
I'm curious, would you prefer an engineer with solid software engineering and design knowledge, or one with minimal experience building real software, but very in-depth algorithm and CS theory knowledge?
I completely agree. However, 1) Google---whose CTO is the originator of the claim in discussion---definitely works on hard algorithms and 2) you never know how your product could grow. A feature which could get dismissed as "impossible" might be implementable by the right talent. Presumably these teams don't need to be full of top algorithm folks.
> I'm curious, would you prefer an engineer with solid software engineering and design knowledge, or one with minimal experience building real software, but very in-depth algorithm and CS theory knowledge?
Depends on the size of my team and what I'm trying to deliver, I suppose. In general I'd aim for a balance in the team, maybe 3/4 engineering/design + 1/4 algorithms. I feel like it's easier to learn design patterns than how to use algorithms creatively.
True, but Google also has over 20,000 engineers working on various things. I think only a tiny minority of those are actually working on hard algorithms.
I have had top algorithm people work for me and they can do make magic happen. But they tend to have the quirky personalities of Wizards. I once had a amazing code ask me if he could so some side work so that he could pay off his credit cards, sure no problem. Next week he came back and told me how he had bought a new 10k telescope....
There are plenty of people who can do really hard algorithms but may need more time or research. Real life doesn't always present you with the same canned problems that are used in competition and you're not operating under the same constraint. A competition problem typically starts from an algorithm, somewhat like a comp-sci exam question. As a competitor you need to quickly recognize the algorithm and then quickly write an implementation. I'm sure a lot of the best competitive people would draw a blank when presented with a problem that isn't a well known algorithm and certainly wouldn't be able to solve it within the time constraints.
Basically it's a game.
What I'd say is that outstanding performance in programming competition probably correlated to some degree with intelligence. Intelligence correlates to some degree with being a good software developer.
I wouldn't hire an Olympic medalist runner to do pizza delivery because: 1) the person will probably get really bored (and may quit or perform bad after some time), and 2) the delivered pizzas will probably be a mess inside the box.
I believe programming competitions would do itself a favor changing its currently ambiguous name to algorithmic competitions. Then engineers, who are also programmers, would be ok with it and we would stop having this kind of threads every few weeks.
That being said, I personally enjoy competitive programming and I do agree that the knowledge you acquire is invaluable :) and I also think that most engineers should practice with online judges now and then to be better at their jobs.
I think it's a bit more complicated than that. What if Olympic medalists were conditioned to think that delivering pizza is the coolest job that exists? What if their scooters have massaging saddles and 5.1 hifis?
I definitely agree this market (segment) has, in a way, a very misled attitude/approach, but definitely, I think there is an (evil) art in marketing such workplaces.
anecdotal_evidence+=1 : Am engineer, only algorithmically interesting task I had the last three months was to quickly find all minimal solutions of a given instance of a certain class of constraint graph problems. That's about average, at my job. Four algorithmically interesting things a year. And I guess I might even be one of the luckier engineers.
a) it won't be trusted
b) it won't be maintainable by any random staff, which puts an additional risk on choosing that solution.
The maintainability argument will usually be enough to can any idea that only few people are comfortable to make evolve.
A lot of them say to want 'A players', but the organization is not ready to value people willing to rock the boat, deeply challenge assumptions or willing to put higher people under scrutiny over their decisions.
Same with hiring super smart people but not wanting to commit to risky bets or to fail often.
In these companies the most demanding people come and go, and more lenient people ('B players' ?) stay to work within the system or try to make things better more incrementally.
I think there are a few very specific skills you learn in programming contests that are useful in the real world:
1) Once you understand the problem to be solved, you can simply sit down and write the code to solve it. The ability to turn ideas into code in minutes instead of hours or days is invaluable. It lets you try lots of ideas, because the implementation step is easy. You can pick the ones that work and throw the rest away, and you don't mind throwing them away because they weren't a big investment. You don't realize how useful this is until you start meeting people who can't do it.
Our coach always told us that there is at least one easy problem, and someone's job the moment the contest starts is to identify it and bang it out. If we hadn't solved one problem in the first 20 minutes, we were already losing.
2) You learn to write code that is simple and correct the first time. Idioms that avoid special cases. Simple data structures ("No pointers!" was common advice, though of course reality was more subtle than that). Understanding when big-O complexity really matters, and when you can use a sub-optimal solution that's easier to code and reason about.
Sure, someone who is smart can write crazy complex code that only they can understand. But crazy complex code doesn't win contests. If you make a mistake you will burn all of your time trying to figure out what you did wrong, and the worse the code is, the worse of a hole you will dig yourself into. Write simple code that doesn't leave much opportunity for mistakes.
There's a scene in Jack Reacher where Tom Cruise's character says, "James Barr is a sniper. He's not
the best. He's not the worst. But he trained non-stop for two years. What does training like that do? What does any training do? Skills become reflex, muscle memory, you do without thinking. It also makes people who aren't necessarily smart seem smart by beating some tactical awareness into them." That's what contest programming does.
3) You learn to identify your limits. There's usually (but not always) at least one problem you should just throw away as "too hard" for the time allotted. You won't solve it, so any time invested in it is a waste. In the real world, where you can't just decide not to do your job, that means you know the difference between when you can go fast and when you need to slow down and reflect. When you need to write a test to really know if the code you just wrote is correct. When it's better to step back and simplify the problem, rather than beat your head against it.
Yes, people like to focus on the "hard algorithms", but really this is not what makes good contest programmers. People aren't deriving Ford-Fulkerson on the fly during a contest. They memorize a set of canned algorithms and apply them when necessary. You learn to recognize and value simple, robust solutions to problems. You carry a toolbox of these wherever you go. This toolbox keeps growing long after you stop competing, and it contains a lot more than fancy algorithms.
Now, there is also plenty you don't learn doing contest programming that you still need to know in a real job. But these are the things I learned that have helped me.
I'm still waiting for the day I need to compute the coefficients of a rook polynomial, though.
I did a bit of programming contests in school as well and most kids practiced hard and got good at the tricks and patterns of solving those problems. The algorithms were mostly undergrad or grad level at best. They were not breaking new ground.
As to whether top ICPC talent correlates to IQ and capability, Most likely, although the same can be said about a really high GPA though. People like Tomek Czajka will shine in all areas.
I've worked at a couple of the Big-4 companies before and the best programmers I met there were not competitive programmers.
This involves some team work
Context matters a lot. One does not use an AK-47 to kill a mosquito. It is terrible for that job. But that does not make AK-47 a terrible weapon.
Good at programming competitions does not equal good at any programming job, is a more appropriate sentence.
A programming competition evaluates almost none of the traits thats doing a marathon run as a highly preforming team.
Kind of ironic for a company whose product values are so tightly tied to quantitive data.
Another assessment could be that a divergence of opinion among interviewers is itself a positive sign -- programmers with strong controversial opinions who are willing to hold to them even in an interview setting might be better programmers for that.
A less sanguine assessment is that "success at Google" correlates with people who generate controversy around themselves, simply because that is something that creates visibility.
After looking at thousands of interviews on the platform, we’ve discovered something alarming: interviewee performance from interview to interview varied quite a bit, even for people with a high average performance ... roughly 25% of interviewees are consistent in their performance, but the rest are all over the place. And over a third of people with a high mean (>=3) technical performance bombed at least one interview.
Everybody else ran with that metaphor. Someone asked what a lion was doing in a forest; don't they live on the savannah? I asked whether he was sure there was a lion; plenty of times I've been sent to kill a lion and ended up having to kill a goat or an elephant instead. Are we even sure it needs killing?
I think that's the difference between a competitive programmer and a professional programmer. The competitive programmer will be much faster with a solution to the given problem, but the professional programmer will solve a better problem.
Assuming you're in the US and somebody tells you there's a lion around, pretty good odds it's not one of the big orange ones.
EDIT: Bay area published yesterday http://www.nbcbayarea.com/news/local/Several-Mountain-Lion-S...
The guy has a brilliant mind. But he doesn't understand the big picture or why his code would fail when integrated with big codebases.
He thinks his work is "done" when he has written his tiny little code with some queues and trees. Then he leaves it for someone else to integrate and thus really solve the problem.
The best engineers I found were the ones who took ownership of their projects and had the ethic to dig deep. Not the ones who could solve toy algo problems.
Ability to work under short time constraints (probably good), to hack out some solution that will work temporarily (good) but probably not solid (bad), to forego much time to consider the implications of design and implementation choices (bad), to develop without communication if solo competition (bad) or without communication outside of the small group of core developers (bad), to build a solution without getting feedback and refinements from stakeholders (bad), and so on.
I suspect that the people good at programming competitions could easily perform well on the job, if the motivation were there. I don't think it necessarily has anything to do with short-term versus long-term problem-solving focus.
There are plenty of short-term problems that you have to solve on the job to be effective. You're not doing them in competition against anyone such that if you procrastinate, you will lose, so the motivation vaporizes.
Also, since job interviews are like programming competitions, people who are good at programming competitions figure they can easily get a job anywhere, and to do that repeatedly. They are not motivated into working hard by job security concerns.
On the other hand, something like Project Euler or even Advent of Code is very nice, if you do it at your own pace, or for learning a new language.
They practice writing correct for-loops, branches, recursive functions, efficient graph searches, input-output, data structure uses etc. in a very fast pace. To be able to do that requires you to chunk a significant amount of information.
I found that doing stuff very fast correctly makes you learn and internalize concepts quickly and more deeply.
This is all unfortunately anecdotal, I'm not sure how to google for such research (not just in cp but in "learning to do stuff fast improves learning rate").
Yet every instrument I've played and tried to learn, the moment I tried to play stuff fast but correctly (be it drums or piano), I had to improve my technique, had to internalize the rhythm patterns (more complex rhythms are insanely difficult to do fast), memory etc. With it came a significant amount of progress.
Same thing happened to me with language. I was speaking English for 20 years but still had trouble with fluent pronunciation (despite my writing feeling natural), I knew what I had to say but somehow my tongue got all tangled. Yet, when I tried learning some rap songs that use insanely fast diction, my speaking improved up to a point where it felt normal.
When you have that mind set and start dealing with a codebase as monstrous as Google's it sounds like a recipe for some serious technical debt.
Why even bother with measuring things that are N-degrees removed from actual engineering, when you can just get people to engineer things?
I know others on HN have been hammering this point home for years, but until something changes it deserves to be repeated ad nauseum: work samples work samples work samples work samples.
Stop with the trivia questions. Stop with the contrived algorithms questions. Ask people to design systems, ask people to defend their designs, ask people to write real runnable code that directly relates to the work they will be doing at your job, ask people to review a real piece of code written at your company, ask people to critique real design produced at your company. Anything but what we're doing right now.
Many good engineers, especially those who aren't young men, don't have existing work samples they are able to share. Perhaps, by choice or otherwise, they have a personal life that does not allow them to write code for free. And their employers won't allow them to share work code.
And many good engineers are not willing to spend an unpaid weekend writing code auditioning for a job they aren't yet sure they want.
And, as you know if you've ever worked with a disruptive colleague, or had customers, engineering in a team for a product is much more than just writing code.
A work sample is a (usually, hopefully) standardized piece of work you request from all candidates that mirrors the actual work they'd be doing on the job.
There's no reason a work sample needs to be so onerous that it costs you a weekend. What people forget when this comes up for discussion is that interviews are work. In fact, they are themselves onerous work, since they require you to be on-site and intensely engaged in ways you don't have to be to do the actual work of a job.
A work sample, on the other hand, can be done from your home, with a beer next to your laptop if that's how you roll.
The notion of audition work has definitely been abused in our industry. People get work samples that aren't standardized. They get work samples that are later ignored. Work samples don't have objective scoring rubrics. Some annoying companies assign new features for their products as "work samples".
Done carefully, though, with objective and predictable grading and calibrated to offset in-person interviewing time, they're superior to any process we have.
The major pain point in interviewing (at least for me) is the massive time sink for a very small chance of success. I'm spending a few hours on the application, then a few more doing phone screens, then a weekend doing a work sample, then I blow a VACATION day (which has monetary value and of which I have only 10 per year), and at the end of it all who knows what my odds even are? Multiply that by, lets say 10 companies per year, and: I've invested 31 days of my life, have no vacation left, and I still may end up with nothing.
On the other hand, I agree wholeheartedly with people who reject "homework assignments" layered on top of a standard interview process.
The onus is on people who want to take advantage of work samples to:
* Ensure that the time they're asking of candidates is offset by lowered time demands elsewhere
* Ensure that they're using the work samples objectively, so that people aren't asked to do coding work as part of a crap-shoot application process.
The process we used at Matasano and NCC was less demanding than typical job interview processes. The challenges were simple and self-contained, and when they were completed we could tell you with a decent degree of confidence whether you were likely to be offered a job at your (shortened) on-site interview.
What do you end up with if you filter out companies that ask for work samples?
It may in fact be a better hiring practice in terms of its ability to predict job performance. But you will _lose good candidates_.
And in particular, you will lose experienced candidates. You are embedding biases toward younger (and therefore less experienced) male coders in your hiring practices if you require what is, from their point of view, free work. (You can see the biases in your "with a beer next to your laptop" remark, for example.)
Source: I ran a work-sample recruiting process for the largest software security company in the US (after it acquired my startup, which was one of the top 5 in the US), and our process most definitely was not biased to younger workers. Work samples drastically improved diversity.
You've edited your own comment again, which makes you very hard to respond to. If you want to rebut anything I've written so far, can I ask you to copy it into a reply to this comment?
- unpaid weekend updating resume
- unpaid weekend practicing algos
- unpaid weekend browsing job sites
just as easily? I don't think asking job seekers to be interested enough to give a work sample is an undue burden. If that filters out people playing the numbers by applying to every opening...is that such a bad thing?
If I spend my weekend on a work sample assignment, it's only applicable for that one company which might very easily not give me a compelling offer or be somewhere I'd want to work.
At this point, I'll only do work assignments if I'm already very familiar with the company and have reason to suspect they'll give me a compelling offer.
If the potential employer is asking for code samples up front, I think that's a bad sign. Let us talk about the work environment, expectations of the position, and then we can focus on my tech skills.
Younger maybe, but where does the male part come from?
I'm the tech lead for a startup with just a handful of programmers. I'm interested in hiring someone who can help us come up with well-architected, novel solutions to the problems that we're already solving. The issue is, we're solving them in a way that turns out to be pretty difficult to maintain and change. I'd like a totally new, fresh approach that takes the lessons we've learned and creates something that's much easier to write fixes for, much easier to extend with new features.
I need someone who doesn't just build on top of existing, well-defined codebases/APIs. I need someone who has the ability to engineer a complete, well-rounded, extensible codebase/API himself.
It would seem to be remarkably difficult to test for this ability. Even work samples that can be done in the short time period you describe wouldn't be able to test for this ability.
Ultimately the problem you're really describing is the challenge of hiring people you don't really know how to hire. If you know very well how the role you're hiring for is supposed to function, you should be able to generate challenges within whatever constraints your candidates have. If you don't know very well what the capabilities of that role need to be, no interview process is going to work reliably for you.
† (we didn't care, by the way: if you did well with that work sample, it mattered very little to us whether you were prepared when you started it).
I think these are two different skills. Some programmers are better in building on top of existing codebases, some programmers are better in build things from scratch.
maintaining vs starting.
What I mean to propose is that we dramatically alter the current on-site interview process used by most companies.
Instead of 5-7 interviews, consisting of 5-7 independent but ultimately equally contrived algorithms questions on a whiteboard, use the several hours you have the candidate to produce real, working code that reflects the work that your company does.
Which is to say, this requires no more time commitment than the existing interview processes at typical tech companies.
If the work your company does is deeply algorithmic, this will be reflected in the work sample produced via this process. If the work your company does is more heavily UX-oriented, this will be reflected in the work sample produced, also.
Instead of "over the course of a full-day on-site round of interviews, candidate produced several short snippets of hand-scrawled code solving various CS textbook problems, which we will now use to infer general engineering ability"...
... you can say "over the course of a full-day on-site round of interviews, candidate designed and produced a module of code that does [X small thing that company does], which can be assessed with the same rigor and methodology we already apply to our existing work and employees"
The way companies in this industry conduct interviews and measure performance is like an aerospace engineering company testing candidates for satellite engineering on their understanding of the Standard Model (physics) or group theory or some such. There's too narrow a focus on what is required of an engineer in the software industry.
This, at least, should be fixable.
Don't work at places that won't let you talk about your work and demonstrate your competence.
My favorite interview process (given by the company I currently work at) consisted of one coding prescreen that wasn't terribly difficult, one session where I simply talked with two engineers about past projects I worked on, and then one higher level architecture problem that involved pseudo-code but not some esoteric algorithms.
That interview process, I think, is way more representative of my actual day-to-day work than any algorithm interview I've done. It deemphasizes the ability to figure out / reguritate previously memorized tricky algorithms on the spot (when do you ever need to do that during the work day?) while emphasizing the ability to communicate how and why you make decisions.
Which is an incredibly important part of engineering, way more important than whatever skill whiteboard algorithm interviews test.
I'm a far bigger fan of at-home coding exercises. Yes, I know, some people get annoyed at these because they think you're asking them to do free work in their spare time. But what other way is there to test people's coding under circumstances that most adequately mirror those of an actual job. E.g. they have a (relatively) unlimited amount of time, they have access to Google, their IDE, etc.
I'd much rather see what someone can come up with, in response to some novel problem, based on a couple of days programming, than what they can hack out on a whiteboard based on thirty seconds of thinking. The former seems closer to what coders are actually required to do, day on day.
Well, constantly, actually. Although I'm thinking of designs and snippets rather than actual functions. I guess it depends on what you're asking people to whiteboard during the interview. I agree that asking people to whiteboard qsort is silly, but walking through design alternatives with occasional code snippets to illustrate implementation options is a pretty basic skill.
> based on a couple of days programming
Either your company is very well-known and very attractive to candidates, or this is going to incredibly restrict your candidate pool.
I think smaller work samples are a great idea, I think code reviews are a great idea, but asking for two days sounds like a bit much, especially early in the process.
But that's the thing -- at whiteboard interviews, they don't ask you to produce "designs and snippets". They make you write actual working classes and functions.
Fairly regularly. Like when I'm explaining to someone how something works after they've asked me about it.
I'd also not consider it to be an engineering job. What you describe seems to me to be more like a CAD technician job at an engineering firm.
That being said we do use the method where I currently work to interview. However, the problems are not some made up situation or a test of data structures/algorithms but a set we've encountered in real life.
From what I've heard the whiteboard method isn't really popular with many companies today either but everyone is sticking to it until some other alternative which avoid false positives presents itself.
I really wish you and so many of your colleagues would spend a few weeks with engineers in the physical science engineering disciplines. What we do in this industry is a farce, with respect to engineering.
But the vast majority of the time -- that's not what your company needs at all. You need someone who's smart and reliable, and more to the point, believes in your mission. And who will go in and do all that far from algorithmic, highly unglamorous stuff that keeps your business from going underwater, day in, day out.
But of course - those skills are difficult to evaluate in a short amount of time. So instead companies go for a skill that can be "measured".
A skill like -- you guessed it -- algorithms.
Is it, now? The best thing you can say about is probably "it seems to kind of, sort of work" -- at the cost of burning through a whole lot of candidates, and their presumably worthless time.
no top notch engineer is going to do a 8 hour take home test for every interview
Yup, take-homes suck, also. But that doesn't mean that complementOf(8-hour-take-home) is the right answer, either.
1) how well they work with others?
2) how long it took them to come up with the solution?
3) if they actually came up with the solution themselves
Whiteboard sessions don't tell you that either. Only whether they got lucky, and you picked a problem they already know (so they can breeze through the solution in front of strangers with confidence). Or if they can at least suck it up, and pretend they enjoy this nonsense for 45 minutes (or 4-6 hours, depending how crazy your company is about this stuff) to get a job.
Does it matter? Really now. What matters on the job is did you go the last mile, think about the corner cases and pitfalls, and make sure your solution was cleanly coded and documented for the next guy or gal. Not whether you solved that silly HackerRank problem in 35 minutes as opposed to 45.
This one's pretty simple to sniff out, actually: "So I'd just be curious, why you did X here and not Y?" There's pretty much no way a bullshitter can answer that question.
It's a weak signal either way for success or failure at interviewing and being able to do the job. Part of the problem, as we've discussed on HN so often recently, is that a programming interview has to waste time with FizzBuzz-style questions just to flag the candidates with great resumes, transcripts, and phone screenings who still actually can't program a computer to solve even a trivial problem.
Or who get nervous, and freeze up.
- knowledgeable at algorithms and data structures
- good at analyzing correctness and edge cases, even on simple non-algorithmic problems (e.g. FizzBuzz)
- accustomed to working hard and learning new concepts. this attribute is not specific to competitive programmer – for example, I'd expect similarly from an open source contributor – but it's higher than for a typical college student.
Some negatives I'd expect, which are fixable over the course of the course of their career:
- over-confidence in code, under-testing
- less skilled in OOP, coding style, version control systems, as well as web development or systems code (unless they have specific previous work in these areas)
- sometimes looking down on gruntwork/rote as beneath them (like the view that pure mathematicians have towards applied math or statistics)
I think that list of positive attributes often outweighs the potential negatives, especially during an internship or in the first year or two of someone's career. After that, I would expect many non-competitive programmers to have picked up on some of those advantages (code correctness, learning new concepts).
I've tried to steer my own interviews away from algorithms (especially DP) and focusing more on giving problems that are relatively straightforward, while still being complicated enough that someone has to write precise code and identify/fix a few edge cases.
Does being good at programming competitions make you good at interviewing for programming jobs?
Obviously there's some irony in that question. But I also think there's some truth to it as well.
I've seen this video before, and IMO the extreme similarity between programming contest questions and Google interview questions could explain the negative correlation. Specifically, borderline engineers with programming contest experience are more likely to get hired than borderline engineers without programming contest experience, and therefore the set of Google engineers with programming contest experience includes more borderline people than the set of Google engineers without programming contest experience. Thus the slight negative correlation.
Just about anyone can get exposure to a set of representative coding questions (see: the USACO training robot, or Cracking the Coding Interview), but training for these contests means spending XX hours a year under a time limit trying to write code from memory (because you don't have time to look things up in the manual).
I don't think that being good at contests makes you bad at the job, though. Consider the following scenario: assume that skill of being good at contests is completely orthogonal to being good at the job (so the real correlation coefficient is 0, instead of negative, observed by Norvig). Then, since it's easier to to get hired if you're good at contests, the observed correlation coefficient inside a single company will be negative, due to selection bias. Depending on the effect sizes, the selection bias might even make observed correlation coefficient negative even if it's positive in reality (which I think it is).
For a more intuitive explanation, consider a used car market buyer who has preference for black cars (this corresponds to algorithm-based interview process of companies like Google or Facebook). Even though the color of the car should have no correlation with its quality, the black cars owned by that buyer will more likely be worse quality than non-black ones.
This is well known in SAT scores. SAT math and verbal scores have a very strong positive correlation in reality; inside almost all colleges they are negatively correlated, because colleges impose threshold effects below and above. (That is, colleges admit students in a narrow band of SAT scores, rather than admitting students who are above a minimum threshold.) You can stylize that into the idea that total SAT score is constant for the students in any given college, which easily explains why component scores would be negatively correlated within colleges.
Excelling as a communicator, being an empathetic person, and having great interpersonal skills are just as (perhaps more) important than how well you can code.
When you allow that anyone to make an account and use your application sometimes you couldn't rely on enterprise technology and it's better to write everything from scratch or to customize open source solutions.
What they are testing in algorithms contests:
1.) the algorithm is correct with various test sets
2.) the performance of the algorithm, running time in milliseconds, there you should to know how to measure time complexity of algorithm with O(N)
3.) memory consumed, there you should know how much memory you are allocating for variables, for example a variable of type byte consume 8 bits
I think that programmers which has prizes in algorithms contests are suitable for business to consumer applications because there they will find a challenge, this kind of programmers will be bored to dig in enterprise frameworks.
In fact 5 of the past 7 years 3pt shooting contents winners were present in the NBA finals this year.
James Jones, Kevin Love, Kyrie Irving, Marco Belinelli, Steph Curry, Klay Thompson, Eric Gordon
That's probably not a coincidence.
It looks like it wasn't "being good at programming competitions" that was negatively correlated with job performance.
It was "participated in programming competitions".
And there are some more "how to interpret machine learning models" caveats in that blog post.
It seems to me the biggest factor in explaining this is that the people who are just below the hiring line but participate in competitions get a bump over the line. Since there are more people just below the line than above it, the "participates" group is bottom-heavy, producing the correlation.
I do a lot of interviews, and it seems to me that lots of people with experience perform below how they "should", because they're not practiced at solving problems from scratch, they work all day on modifying larger systems. Programming competitions would fix that for them, as would most open-source hobby projects.
If I was interviewing someone, a career as a competitive programmer would not be a detriment but it would not count for very much overall. We are looking for creative thinkers that can work as a team.
It's probably something you might list on a first job resume but further on down the road, you'd just list your work and your education.
All proxies are inaccurate.
In my experience, people merely hoped their process was good enough. And it usually was. The "we only hire the best" is marketing propaganda and they know it.
I posit that one of the dangers of spending a lot of time doing programming competitions and becoming very proficient at them is that, perhaps, you can come to believe that "true" programming, some sort of Platonic ideal of programming, is about coming up with the clever insight that solves an algorithmic puzzle.
But, in fact, a fair bit of _commercial_ programming is down and dirty, with databases, and user interfaces, and a lot of the time is really just shuffling data from one place to another, maybe filtering it or combining it with another set of data.
And that's just at the beginning of your career. Later on in your career, success means being able to work at larger scales in a team. That means organizing the code in a way that supports the efficient development of the codebase by individuals like yourself, by your team, by the development group as a whole... And at the architect level, you perhaps are looking at designing the system to support the efficient operation of the entire organization.
So I can easily believe that success at a programming competition does not correlate with long-term success as a software engineer in commercial software development. The two are really very different.
(Btw, I actually found the competitions that I did to be fun, but mentally exhausting. I'd say go ahead and do them, especially if you have an inclination for those types of problems. Just be prepared to use a different mindset for commercial software development.)
Candidate having competed in programming competitions - big green flag.
But, if success in competitions is, in their view, their biggest asset - amber flag.
I would extend this to most competitive endeavours.
I'd rather pick an algorithmist and teach him/her to reflect than a so called "reflectionist" / "slow coder" and teach him/her how to solve algorithmic problems.
I'd be interested in knowing the guy's (catonmat) own reflections and experiences too.
I did so in response to the CTO of Kaggle tweeting "Super confused why we still use resumes. Get 100x the signal from domain profiles (GitHub, StackOverflow, Kaggle, etc.) & real work samples", which ... where to start [https://twitter.com/benhamner/status/883137638084956160].
"being good at programming competitions" hugely positively correlated with "begin good on the programming job" unconditionally