Current candidate sourcing and matching methods need to be revisited, as more than 70% of the candidate applications that HR receives through initial screening are not suitable for the job. Recruitment platforms heavily depend on keywords to identify whether the candidate matches the job profile during sourcing and initial screening. This leads to overall hiring process taking around 30 to 40 days to hire a single person, and its observed that good amount of time goes into sourcing and matching the right candidates for respective job positions.
Every extra minute it takes to find a right candidate, it gives competitors an opportunity to select that candidate. With the current rise in hiring, the amount of time required for hiring is also increasing, so is the work pressure on the HR professionals.
Most of the times, it’s the HR professional who has to decide, based on their understanding from job profiles, past interviews, and available candidate pool. With increased hiring and candidate profiles, it becomes almost impossible to evaluate each profile carefully before moving it to the next stage in the hiring pipeline. This in a way affects the quality of matching the right candidates as well results in increased hiring time. So it’s becoming critical to assist HR professionals in some time-consuming activities, and make existing recruitment applications smarter.
To solve this, machine learning based recommendation engines can play a big role. These intelligent recommendation engines can save HR professionals time needed to go through multiple resources per candidate; instead, it can easily mark candidates that are recommended for given job position using Machine Learning (ML) algorithms. Machine learning can process past data to devise a method of screening candidates based on historical candidate screening decisions made by HR. Two important data points for the ML would be the resume of the candidate and job description. As this data is unstructured in nature, the process can make use of Natural Language Processing (NLP) techniques to extract the features or key points from resume and job descriptions. ML algorithm then will use these features to find the candidate fitness score with respect to a job description.
The similar technique can be used for sourcing activity to leverage the data available on various social media sites to find out candidates that match the top performing candidates in the organization. Using the digital footprint of the candidate, it can devise machine learning models to find out right candidates automatically and HR can connect to the candidate at right time.
To summarize, machine learning can automate the time-consuming task of candidate sourcing and matching to save almost 60% percent of recruiter’s time as well as can improve the quality of hire. At Harbinger Systems, we have helped multiple HR tech customers to modernize recruitment platforms by implementing intelligent recommendation engines, to know more please contact us.
