Finding suitable candidates for a particular position is a difficult task, especially when you have received so many applications. It can increase the effectiveness of your team if you have the right candidate at the right time. This is the reason why MNCs like Google are using machine learning for resume screening. In this article, I will walk you through the process of Resume Screening with Machine Learning.
Why Machine Learning in Resume Screening?
Talent acquisition is one of the most important and time-consuming tasks in the HR department. If I’m just talking about the Indian market, every year we see a million more people entering the market for new jobs. According to Linkedin, the Indian has the highest percentage of the workforce that is actively looking for a new job.
In such a situation, the most difficult part for a company is the lack of structure and standard format for the resume, which makes the preselection of the desired profiles for the required roles very tedious and time-consuming.
The Resume Screening process requires domain knowledge, which means that the recruiter must be able to understand the relevance and applicability of the profiles to the positions. But with the number of different job types that exist today, as well as a large number of applications received, it is difficult for the HR department to choose the right candidate.
These problems become even worse if the human resources department lacks knowledge of various skills and knowledge in the field. This is where machine learning can be used for the CV filtering task. In the section below, you will learn what are the issues that can be overcome by using machine learning in Resume Screening and how.
Resume Screening with Machine Learning
Through the use of machine learning algorithms, we can weed out all irrelevant profiles as early as possible, which will also save money on the process of recruiting new employees. Below are the top three reasons machine learning is used in Resume Screening:
- Separate the right candidates: If I take an example from India, it’s a huge job market and millions of people are looking for jobs; it is humanly impossible to screen every resume and find the right match. This makes the entire hiring process slow and cost-inefficient for businesses. Using natural language processing to identify the candidate’s concentration skill will automate this process.
- Giving meaning to the candidate’s Resume: The second challenge is posed by the fact that market resume is not standard almost all market resume have a different structure and format. HR has to manually scan CVs to find the right match with the job description. This is resource-intensive and prone to errors as a good candidate for the job could be missed in the process. Again, with the use of machine learning algorithms, we can automate this process.
- Knowing that candidates can do the job before they hire them: The third and biggest challenge is matching the resume to the job description to understand if the candidate would be able to do the job they are hired to do.
So now you know how and why machine learning is used to screen CVs by big tech companies. You can learn more about it practically from below by working on a machine learning project on Resume Screening using Python.
I hope you liked this article on Resume Screening with Machine Learning. Feel free to ask your valuable questions in the comments section below.