How machine learning is disrupting traditional recruiting practices

December 11, 2017
An average of 250 resumes are received by recruiters for a single job position and it takes about 42 days to fill an open position.

A majority of companies invest a lot of their time as well as resources to hire the right candidate for the organization. Money is spent on advertising about the job opportunity, and connecting with contractors who can provide access to more talent.

But what if you realized that you already have access to the best candidates, but you didn’t know how to reach out to them?

Machine learning and recruiting

The biggest problem that recruiters face today is an absence of quality time. With ample progress in the digital sector, the world has indeed come a lot closer because of easy networking opportunities. Your company may have more than 4000 LinkedIn connections, but do you have the time and resources to utilize these connections to reap the benefits?

The answer is no.

And this is where machine learning comes into the picture. It takes in important data into consideration like a candidate’s profile, work history, experiences, contact information, etc, and then matches it with the relevant job opportunities. Machine learning will itself not select the best candidate, but narrow down the choices and provide you with the recommended candidates, fit to be interviewed. This not only promotes stronger hire but also ensures a higher ROI.

Machine learning can be implemented in the hiring process in the following ways -

1. For scheduling interviews

The entire process of scheduling interviews for multiple candidates can be a tiresome and time-consuming job. Moreover, the negotiation regarding the time and place between the recruiter and the candidate is often dragged for long.

With the help of artificial intelligence and machine learning, you can manage the scheduling of interviews within a much lesser time.

At, candidates attend the pre-screening interviews at their convenient time and recruiters can listen to those recorded interviews at their own convenience. As a result, a lot of unnecessary email correspondence can be excluded and a lot of time is saved.

2. Proper use of semantics

Think of a situation where you are recruiting for a senior marketing specialist position in your company. For obvious reasons, the best and easiest way to recruit a candidate for this position is by scanning resumes of candidates who have experience in this field and have held the position of a marketing specialist in the past. Now, in the field of marketing (or any other field too), there may be different job titles for the same work responsibilities. But the system will discard resumes that have different titles like Marketing Manager or Marketing Coordinator. Moreover, even among the selected ones, every resume will not have the job details exactly matching your job description.

All of this results in you losing out on potential good candidates.

The good news is, with machine learning, this problem of language and semantics can be addressed. With the help of conceptual tools, machine learning tries to comprehend what a recruiter is really looking for, without focusing much on the worded query. Instead, hiring managers can select and mention a few keywords for the role in th job description, and the smart technology will itself derive conclusions regarding the suitability of a candidate by scanning through their resumes.

This way, candidates who have mentioned their job posts differently in their resume but are qualified for the role will be selected and candidates who may have the matching job post but not the correct experience will be eliminated. The end result - you get to recruit the best fit for your company!

3. Smart sourcing of candidates

With AI and machine learning, there has been a great impact on candidate ranking and searches. The technology will analyse resumes for specific words/phrases from which it will determine the proficiency of the candidate (for example years of experience, the capability of using certain software, etc).

Interviewfox uses the same machine learning technology to analyze information derived from resumes sourced from multiple channels like job boards, resume databases and social networking sites, before coming to a conclusion regarding how well-suited a candidate is, for a particular job post. Recruiters will be provided with a recommended list of candidates ranked as per relevancy.

Does machine learning put an end to human recruiters?

The belief that machine learning can completely replace human recruiters is not a very realistic one. This is all about technology being applied sensibly, and not about aiming for a completely automated hiring process.

The human element present in the recruiting process is important both for the candidate and the recruiter. Technology will only help in streamlining and simplifying the hiring process, because no matter how advanced the process is, ultimately nobody wants to get hired by a robot.

Using AI and machine learning will only ensure that you can select and retain the best candidates for your organization. Register on today and hire the best fit sourced from a wide talent pool.