Calculate Candidate Compatibility Percentage for a Job Position (NLP)

Utilizing NLP and ML Techniques to Assess Candidate Fit for Job Roles

This topic will be divided into two sections due to its length. The first section will focus on understanding the data, exploring the types of values present, analyzing the distribution and correlation. It will also include a data cleaning phase to ensure the quality of the information. On the other hand, the second part will be dedicated to creating a machine learning model. This blog will exclusively focus on the first part of the process.

Introduction

Big companies like Google face challenges when it comes to selecting suitable candidates from a broad array of resumes and job descriptions, leading to time-consuming manual reviews by human resources managers. The influx of resumes through job platforms compounds this problem, requiring significant human resources and introducing potential biases.

Leveraging advances in Natural Language Processing (NLP), particularly transformer and attention-based models, can expedite and enhance the candidate selection process, saving time and money for companies. By calculating match percentages using machine learning models, companies can efficiently identify the best candidates, simplifying the early stages of the hiring process and guaranteeing a more precise evaluation of candidate suitability.