Compunnel’s machine learning optimization speeds up candidate screening by automating important tasks like screening resumes, identifying high potential candidates and find behavioral traits that fit well with client’s culture. Using machine learning algorithms, our machine learning based filter analyses profiles from all our sources like iEndorseU, Jobhuk, our proprietary database and profiles from our specialized recruiters. The following are some of the benefits our machine learning optimization imparts to the recruitment cycle.
Trained on 28 million job descriptions, 12 million candidate profiles and more than 1.4 million candidate interactions, our machine learning based filter screens the best possible candidates in the shortest time.
Figures received from our beta testing suggest that our machine learning based filter reduces candidate shortlist time by at least 62%.
It prioritizes candidates on the basis of their skill set and experience.
Based on the concept of artificial neural network, our filter is not only getting better with time, but also more intelligent. So expect faster and more accurate screening in the future.
After incorporating it into our hiring cycle, we have reduced the mid-project attrition by 99%.
One of the biggest advantages of our machine learning based filter is that it reduces any human bias involved. By not focussing its decisions on the basis of candidate’s gender and race, it encourages a diverse workplace.
The greatest advantage of our machine learning based filter is the flexibility it brings in terms of cost and time. So no need to hire tons of resources or manually screen resumes for days.