Hiring when the company is growing on a massive scale can be difficult for the Talent Acquisition team. Going through 1000s of resumes and profiles to present 100 and then selecting 1 of them involves too much effort and repetitive tasks. But thanks to AI for saving the efforts of TA teams and helping them source the right talent.In conversation with ETCIO, V. Padmanabhan, Vice President – Engineering, GlobalLogic, shares the company is using AI and ML based tools for sourcing talent, screening resumes and getting them onboard post selection.
“We are today looking at massive growth in our teams and this is not gonna happen with somebody looking at the profiles out there and figuring out who is the right fit and then putting them through part A and part B process, this is simply too much to do,” Padmanabhan said.
“So we have put up a lot of AI, ML-based constricts around just the resume validation. We wanted to look at what all certifications the candidate has, what are other companies they have been part of, their research papers basically to generate some kind of summary of the resume just through systems?,” he added.
If a team has to look at 1000 resumes, it is not going to happen just by 10 people sitting around and doing that. So, GlobalLogic has automated the systems to do talent identification for fitment into the roles.
“For someone manually going through the profiles, talking to his/her connections and sources to get good candidates, reading the resumes and matching it to the JD that he has got from the delivery team takes a lot of time and effort. It used to take a week to actually deliver a good profile to the delivery team which is now shortened to giving our TA team a potentially high fit catchment of profiles. So the TA team is now not expected to go through everything, do a keyword search, figure out what all is coming up and read through everything. They now have access to profiles which the system has screened for them,” Padmanabhan explained.
With this, the company is cutting down weeks of efforts to a day or two where the TA team has the access to the bank which is pre-certified by the system.
The AI based approach to Talent lifecycle especially on the acquisition side plays a major role in the Recruitment effectiveness, productivity as well as Talent experience. This enables not only Higher Velocity and faster scaling of teams but also ensures optimization of the time spent by the engineering teams in shortlisting the right talent thereby improving their motivation and productivity.
“While the initial Models resulted in the reduction of Resume perusal volumes by 30% by way of match between the job description and the potential candidate’s skill set, the effectiveness and fitment of the identified repository has gone up by 50% thereby enhancing pipeline to selection ratio significantly. While this is the initial bootstrap model training period, we expect the model efficiency to go higher and impact ramp up velocity between 30 – 40% enabling a much faster onboarding of teams,” he explained.
“We are still working on the system before we say we are 95% accurate but then this is how the ML works and learns. We are a little fortunate that our skill sets are correctly and strongly typed. So we know what we are looking for. And we are now working on improving the selection ratio of these pre-screened profiles,” he concluded.
