Resume or Candidate screening is the way toward auditing employment forms or job applications. Screening resumes are the way toward arranging resumes to discover qualified or disqualified candidates utilizing progressively more detailed resumes examinations. The resume screening objective decides whether a candidate is eligible for a role based on candidate education qualification, work experience, and other useful information by capturing from candidate’s resumes.
There are so many reasons to conduct screening of candidate resumes before recruitment for any company position or role. From those reasons few important reasons when conducting resume screening.
Time is important
Any successful employee knows the value of the time they spend on any task. They want to reduce that time without compromising the quality of the resultant work. Screening resumes are the best way to reduce or save time and money by identifying neither suitable nor qualified candidates for that particular job.
Reduce 90% of applicants
The screening process is proficient in both time and resources due to the incredible number of candidates that you can discard as unsuitable. It’s critical to recollect job seekers will search for positions from various perspectives – from administrations to independent applications. Hence, recruiters need to figure out how to screen through every one of these candidates who will be coming at the job position from all angles.
This is the best way to generate or create questions based on the candidate’s resumes. After screening candidates, we have only a few resumes. Based on that, resumes recruiters can generate different questions for every candidate suitable for that job.
Focusing on further steps
The screening process will offer managers the chance to concentrate on any other process of interviews. Any information captured in the screening process should be effectively utilized and structure the premise of further questioning.
Plenty of achievements in the business world can be put down to making a ton out of the little organizations need to work with. This capacity to boost effectiveness is a great trait, especially in the field of recruitment, and screening should assist recruiters with smoothing out the candidate screening.
Nowadays, the number of candidates for each job post is high, but the recruiter team is less. The workload on the recruiting team will be increased day by day. The manual resume screening process is time-consuming and needs high resources in terms of the recruiter’s efforts.
To avoid these consequences, AI-based models provide excellent solutions or results in the field of recruitment. Like that AI-Based solution also improves resume screening, which is the most important task of recruiting.
Saving recruiters time
Manually screening resumes is as yet the most time-consuming piece of recruiting, mainly when 60% to 80% of the resumes got for a job are unqualified. Screening resumes and shortlisting candidates to interview is assessed to take 23 hours of a recruiter’s time for a single candidate hiring process.
AI-Based solutions can help recruiters in the hiring process if the solution can successfully automate time-consuming and repetitive tasks like a resume or candidate screening. As a little something extra, accelerating the way toward selecting through automation screening reduces time-to-recruit, which means organizations will be less inclined to lose the best talent to quicker moving competitors.
Improve the quality of the hiring process
Information or data has gotten simpler to extract, gather, and analyze throughout the long term. Quality of recruiting has become the top prerequisite of the team of recruiters.
The guarantee of AI for improving the nature of recruit lies in its capacity to utilize information to normalize the coordination between candidates’ work experience, education qualification, knowledge and skills, and the posting job’s necessities. This improvement in job matching is predicted by more productive employees who are less likely to turnover.
The resume screening problem statement wants a bunch of resumes to build an effective model. We can collect that bunch of resumes from companies, especially from recruiting teams, because they receive so many resumes for only a few job postings from candidates. Candidates will upload resumes to desired job posts through the company. After that, Companies or recruiting teams of companies save all those resumes uploaded by candidates, so we can easily collect all those resumes and extract data from all by using different technologies or methods. We are creating a dataset from all those extracted data from different resumes.
End of this data annotation process, extracted data will become more valuable data to our model. This means we can get crucial or essential points from every resume.
In this phase, the annotator will annotate different vital tags useful for extracting top resumes suitable for job posting based on their requirements, such as experience, skills, etc. For this, annotators will annotate the extracted text with different kinds of labels such as
From annotated data, the model will learn all details of every candidate from their respective resumes. That will help models recognize or select top resumes from all based on requirements.
We have to apply a little bit of preprocessing techniques to our extracted data or text from resumes. At the time of extracting text from resumes, we may get extra space. So we have to remove all those extra or additional spaces because we can’t get any useful information from that. Like this, we have to apply different techniques such as removing stopwords, stemming, lemmatization, etc.
The process of screening resumes is automated by using Named entity recognition (NER). Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) and Information Extraction (IE) that processes large amounts of unstructured human language to locate and classify named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It is a supervised learning problem.
By using this named entity recognition technique, the model will identify all key or important features related to the candidate’s resumes. So automatically recruitment teams will be able to identify top priority resumes from a huge chunk of resumes. In this resume screening process named entity, recognition plays an important role.
The topic modeling technique is particularly useful for finding latent patterns in large collections of text by extracting clusters of words that are closely related and frequently occur together.
For example, in a database containing CVs from IT experts, programming skills may constitute a single topic.
There are so many algorithms available to build topic modeling techniques such as Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), etc. we can apply these techniques or algorithms to our huge corpus to identify the number of topics from that.
We can group the different kinds of resumes based on topics that are important to the recruitment team for a particular job description.
After completing model development, we have to test that one. For simplicity, companies can deploy trained resume screening models with a front view such as different styles to look good. For this company can utilize already available deployment technologies such as web applications, docker images, etc. we can integrate the Rest API with web applications for a better view. After deployment, the recruiting team can be screening the resumes based on their job description. It makes their hiring process easy and effective by extracting the required information by named entity extraction. Automatically it reduces the cost of the hiring process. This process will provide potential candidates to organizations or companies.
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