GOOGLE’S Recruitment Machine: The Algorithm Candidate Screening Model

Picture this! If you are an organization which attracts more than a million applicants every year – that is nearly 130 applications per employee – and you are obsessed with hiring ONLY the best-fits; WHAT DO YOU DO? How do you make sure that you build an effective – if not watertight – recruitment process to cater to a strategic business objective of such walloping proportions?

One of the most innovative organizations of our times – GOOGLE – turned to statistics to solve this very problem. Regression analysis to be precise! GOOGLE had made a conscious decision to steer away from the traditional process of shortlisting candidates based on purely academic performances and look for more well‑rounded candidates instead.

“As we get bigger, we find it harder and harder to find enough people,” said Laszlo Bock, Google’s vice president for people operations. “With traditional hiring methods, we were worried we will overlook some of the best candidates.” “Interviews are a terrible predictor of performance,” Mr. Bock said.

The idea was SIMPLE! Based on a comprehensive performance measurement and management system – GOOGLE already knew who the star performers were in their organization. They then just had to look for key personality and character traits seen in these top performers and see which of them were present in the potential new recruits that they intended to hire.

Additional Reading: http://www.nytimes.com/2007/01/03/technology/03google.html?_r=1&oref=slogin

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5 Comments on “GOOGLE’S Recruitment Machine: The Algorithm Candidate Screening Model”


  1. This seems to be a very novel and out-of-the-box approach to employment candidate screening and selection. As a former recruiter, and as someone who has recruited people for various positions over the last 25 years, traditional interview techniques always struck me as terrible predictors of capability, with a potential for both type I and type 2 errors. The interview, in particular, because it is so easy for a skilled and trained interviewee to “fool’ not so well trained interviewers.

    It seems to be a natural corollary therefore that they also are also poor predictors of performance. I neither know, nor am qualified to comment on, whether this is a good method to overcome these limitations, but it does seem to be a good idea to try and find better methods for evaluating candidates.


  2. Dear Mr Bhargava,

    Thank you for your comment.

    I entirely agree with you that this may not be the best process for finding ‘the right person for the right job.’ Not until the results on its effectiveness are out.

    The NY Times article on this subject has Michael Mumford, a psychology professor at the University of Oklahoma who specializes in talent assessment, quote that:

    “This sort of test was effective, but companies should not rely on oddball factors, even if they seemed to correlate to good performance.”

    We will attempt to source some results on the success of the process and present it in a future blog entry.

    Amit GK

  3. Idetrorce Says:

    very interesting, but I don’t agree with you
    Idetrorce

  4. karan Says:

    Hello Sir,
    i like to defer on this for the fact that the star performers in any organization have different and unique qualities in them which make them stand out they may not be really savvy with all the algorithms and apptitude tests which is used mostly to test an interviewee but if given a situation or a scenario to code on they can find a better logic or solution to achieve the desired goal better than the one who is a real ace in algorithms and apptitude.


  5. […] עבודה פשוט. בנושא הגיוס מצויות תפיסות אחרות ומגוונות. בגוגל, למשל, תפיסת הגיוס מעט שונה- הראיון, לעמדתם, הוא מנבא […]


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