AI In RecruitmentHR Technology

The Importance Of AI-powered Applicant Tracking System

By January 13, 2021No Comments
Importance of AI-Powered ATS

This is a Guest Blog by Khushboo Kataria, Data Scientist with TurboHire. Khushboo is has a Master’s degree with IIT-Madras. She has also accomplished Data Manipulation with Python Track with DataCamp.


fnjkvbklfnvkg

Since their advent in the nineties, Application Tracking Systems have come a long way from simple tools to gather and parse resumes to being sophisticated pieces of technology that are equipped with easy to navigate UI, automation, and analytics that can streamline the hiring process end to end. With more and more companies opting for ATS, their indispensability in recruitment is unequivocal.

With the pressure mounting on HR to hire the best candidates in a limited time frame and with the ever-increasing applications for a job, you need an ATS that can understand your hiring needs and can rank and match the best candidates, suggest similar candidates who applied to similar jobs besides the usual automation of repetitive tasks.

It’s practically not possible for a recruiter to go through all the applications even if they are pro in screening one within seconds. You definitely wouldn’t want to lose out on a great candidate because of this impracticality of screening each resume. That’s where AI comes to your rescue. An ATS powered by AI coupled with human intelligence will increase the efficiency of the hiring process manifold.

Benefits Of An Intelligent ATS Platform

Finally, the following three points sum up the benefits of an ATS software that is AI-powered:

1. AI-powered ATS Helps In Saving: An ATS coupled with AI capabilities helps in saving effort, time, and money. Automation helps save time and effort and with AI optimizations, HR can focus on other critical business decisions.

2. Improved Quality Of Candidates: By virtue of ranking algorithms that can evaluate hundreds of candidates within seconds, HRs can now access the best candidates faster, without bias.

3. Increased Productivity: With less time spent on sifting through candidate applications, recruiters can deal with more important tasks like candidate engagements.

The TurboHire Impact

At Turbohire, we use a ranking model based on NLP and machine learning. It takes a resume or a set of resume applications and a job as input and assigns a score to each resume based on how closely it matched the job. The score is curated based on different criteria like skills, experience, and education. One can control how much weight one wants to give to each criterion.

The model is trained using real past data of applicants that applied to different jobs and whose suitability to the jobs were reviewed by an HR expert. The model learns from these expert decisions and keeps improving the score and ranking with their feedback. The algorithms for suggesting similar jobs or resumes use similar logic.