Here's another one showing 75,000 resumes received in one week: http://www.sfgate.com/business/article/Google-gets-record-75...
When you have the luxury to pick the best, from the best of the best, you don't have to follow the advice given by HN armchair quarterbacks like us.
And engineer headcount costs are a huge component of their business --- so much so that they've been accused of breaking the law to collude with other companies to avoid competition in hiring!
Google is succeeding in spite of the waste and unreliability of their hiring processes, not because of them.
But there's no evidence that switching to self-paced work samples would cost them less. With Google's popularity, they'd get more false-positives from candidates that copied the code from widely disseminated previous projects. False-positives cost money.
Your medium size firm with smaller volume of candidates won't have that problem of increasing false-positives.
Sure, with whiteboard interviews, the rejected candidates (and even ex-Googlers) can write a "brain dump" blog with blow-by-blow algorithm questions but history seems to show that these don't work so well as cheating mechanisms.
What kind of work sample project could Google realistically design for 10000 programmers to complete? (It can't be as hard as "solve this Clay Millennium problem" or as easy as "reverse this string". Anything between those 2 extremes is trivial to copy to github.) How often do they need to redesign the work sample? What about objective "comparisons" which was touted as a feature of that method? What about the programmers that don't want to do the work sample? (They do exist!) Is there also a cost to filtering them out?
It's great you're really enthusiastic about work samples and want more companies to adopt it but I see no slam dunk evidence that they are the universal best method for every company.
Google is manifestly too capriciously selective in their interviews. They are infamous for turning down people who go on to do excellent stuff elsewhere. Watching acquaintances navigate their process makes me believe the infamy is well-earned.
That's an argument that is separable from the question of how best to design the Google interview process. I think Google's interview process should be work-sample based, like I think every tech company's should be. But you could argue that either way. We can both agree that Google's challenges are sui generis. We're both reasonable and might disagree on the rest of it.
Where I don't think reasonable people can disagree is on the question of whether Google overpays for engineers. They do. They pay a tax for the luxury of being capricious about who to hire. They can afford to do that, but most companies can't.
One thing that really rustles my jimmies is the constant assertion that "false negatives are (effectively) free". I think Google and the companies who hire like them seriously underestimate how much this costs them, both the direct costs of spending so much to ultimately reject people and the indirect costs from the work that is not getting done or being foisted on another overloaded engineer.
My experience is that running a hiring process that lights up only on the kind of talent that qualifies itself with a standard tech interview is ludicrously expensive. People that do well in standard tech interviews can work anywhere they want. If you can only hire those people, you are competing for talent with the wealthiest (or most overfunded) tech companies in the market.
Fortunately: performance in tech interviews is in fact not a good proxy for programming ability (in fact, there are ways in which being good at interviews can obscure deficits in candidates), and you can get ridiculous discounts to the price Google pays for talent if you don't try to hire people the dumb way Google does.
And really there are levels of bad hire. There's the bad hire who really doesn't know how to do the job and will never get good enough to do it. That person should be very easy to spot with not too much effort in the interview process. The dangerous hire is someone who on the surface can do the work but has a toxic attitude and/or doesn't learn/grow. I'm not sure putting someone through a torturous interview process is going to root that person out.
I think where a lot of the elitist hiring is at now is that many companies aren't so much trying to filter out people who can't do the job, so much as they're holding out for who they think are rockstars. So they put candidates through the ringer with the thought that what will be left is rockstar material.
But many highly-qualified individuals won't jump through hoops for any but the top tier companies, and sometimes not even then. Furthermore, by definition, rockstars make up a very small percentage of the devs out there. What are the chances that every company who is holding out for elite coders even has any applying? Especially considering that elite people probably don't jump around often.
SELECT TOP 1000 FROM resumes ORDER BY received_date
But that's what the definition of "selectivity" is for database retrieval. Selectivity == n_rows_selected/n_row_count. The "larger number" was the denominator and the "small number" was the numerator.
Your example SQL is not consistent with your previous sentence:
SELECT TOP 1000 FROM resumes ORDER BY received_date
For examples of Harvard and Google, we know the denominators (the total applications and total resumes). Therefore we know the selectivity.
I suspect you're mixing up "mathematical selectivity" from "decision process selectivity" because Google's internal decision tree for hiring might look to outsiders as "black box" or nonsensical.
Thank you for confirming that you were using the colloquial version of "selectivity" which doesn't require knowing the denominator instead of mathematical "selectivity" which does.
You're using "selective" like this definition: http://www.merriam-webster.com/dictionary/selective
I was using "selective" like this: http://www.programmerinterview.com/index.php/database-sql/ca...
The following wiki page ranks college selectivity and it absolutely requires the denominator to do so: https://en.wikipedia.org/wiki/Rankings_of_universities_in_th...
That wiki page orders the ranking on mathematical selectivity and not colloquial selectivity. My previous comment of Google Inc being more "selective" than Harvard is to be interpreted as mathematical selectivity. Sorry for not stating it more explicitly to prevent confusion.