Product Design: Reduce Qualified Candidates Wrongly Rejected in Google's Hiring Funnel

You are a product manager at Google. Recruiting costs the company roughly a billion dollars a year. The hiring process has been tuned hard to avoid false positives (people who are advanced or hired but should not have been), and the side effect is a large number of false negatives: strong candidates who are passed over, screened out or turned down. How would you build a product to solve this problem? ```hint Measure before you fix A rejected candidate never shows you how they would have done in the job. Ask how you would even know a false negative happened, and at which step of the funnel it is most likely to happen. ``` ### Clarifying Questions - Where in the funnel is the concern: qualified people recruiters never contact, resumes rejected at the screen, or candidates turned down after interviews? - Is the aim a lower cost per hire, a wider talent pool (for example people with non-traditional backgrounds), or both, and is any rise in the false-positive rate acceptable? - Who is the product for: recruiters and sourcers, interviewers, the people who make the final hiring call, or candidates? - Does this cover every job family and region, or may we start with one high-volume area such as engineering? ### What a Strong Answer Covers - A precise definition of a false negative at each funnel stage, and recognition that the costs of false positives and false negatives have to be weighed together rather than one being minimized alone. - A credible way to measure false negatives even though rejected candidates never show how they would have performed. - A deliberate choice of user and funnel stage, with pain points tied to the specific proxies and habits that wrongly reject people. - A concrete product with a first version, plus safeguards against automated-screening bias and employment-law exposure. - Success metrics tied to false negatives and cost per hire, with guardrails on hire quality and interviewer load. ### Follow-up Questions - After launch, twice as many candidates reach the phone screen, but the phone-screen pass rate halves. Is the product working? - A model trained on past screening decisions would learn to copy them. What would you train it on instead? - How would you win over interviewers and hiring managers who are rewarded for avoiding bad hires and see more borderline candidates as risk?