A consumer tech company in Asia ran a year long search for a senior data scientist. They sourced 1,300 candidates. They ran 250 hours of interviews. They issued six offers. Zero were accepted.
The post mortem was brutal. Their screening was filtering for the wrong things. Resume keywords matched, GitHub repos looked good, but the structured interview signal was not feeding back into the screen. So they kept pulling in the same kind of candidate who failed at the same stage. Round and round.
Screening automation only works if it learns. A static filter, even a smart one, decays. The companies getting this right treat screening as the front edge of a feedback loop, not a gate.
Here is the loop that works. The screen pulls a candidate in. The structured interview generates evidence on which competencies actually matter for the role. That evidence updates the screen for the next candidate. Within 15 to 20 candidates the screen has converged on what your hiring managers actually care about, not what the JD claims they care about.
The dirty secret of most hiring is that the JD and the bar are different. A platform like Mazle surfaces the gap by comparing what was written down in the intake call to what actually got asked in interviews. The first time a team sees that delta, the screening criteria they have been using for two years usually gets rewritten in a week.
Automation without learning is just a faster wrong answer. Build the loop, then automate.
