We are excited to announce that we are experimenting with a robust new feature for JyMob. This is related to providing a quantifiable assessment of a candidate’s relevance to a given job.
Yes, our machine learning algorithms now do a great deal in trying to find out the match between job descriptions and candidate resumes. Of course, we know that a lot gets lost in translation when it comes to job descriptions and resumes, but a robust candidate assessment service should have a way to quickly sort candidates based on their overall relevance to the given job. This is especially useful in high volume cases.
We plan to tweak the matching algorithm in coming weeks and months. If you want to participate in helping us perform better, we request you to take a look and provide some feedback. Seasoned recruiters, hiring managers and engineers are extremely good at deciding the match amidst imprecise data (like job descriptions and resumes), and we want to improve our algorithms to become reliable in deciding a match.
The feature integrates nicely with the list of candidates and is available when the candidates show off their skills on our tests. You should really try it out yourself, but here is what it looks like:
The candidates are nicely sorted on the score and when you click on a particular score, you get the details that went into determining the score:
Nice, isn’t it?
But wait, that’s not all. We encourage you to form the hiring team for your jobs and we also calculate the hiring team sentiment based on how candidate has performed on the display of their skills! Here’s what it looks like:
We hope this helps you in making an informed decision about whether to invite a candidate or to archive his/her job application (which is also a feature I will talk about soon).
If you haven’t already, give JyMob a try. It’s well worth your time :-).