Challenges:
Issues in manual screening:
Manually reviewing resumes, leads to a slow hiring process and missing some highly potential candidates.
Scalability Concerns:
Excessive job applications due to the company expansion affect the efficiency of the recruitment team.
Improper Data Extraction:
Seeking relevant information and insights from resumes will be a challenge due to the different kinds of formats that make it hard to compare and evaluate candidates objectively.
Objectives:
Robust System
The principal goal is to develop a rigorous and robust system to parse resumes efficiently and accurately. Extracting key insights and data such as Candidate name, phone number, email, designation, education details, employment history, and skills is one of the primary objectives users look upon while using an applicant tracking system.
Seamless Operations
To streamline the operations such as the hiring process, saving time for recruiters and providing a comprehensive overview of candidates.
Technology Stack
A wide range of technologies has been used in implementing an applicant tracking system. Python 3.9X, NLP, NLTK, spaCy, and PDF plumber are some prime technologies used that result in easy hiring and recruitment operations. Python 3.9X is the last version that provides Python 2 backward compatibility layers. spaCy is a key technology used in this integration which is an open-source library for natural language processing.
Other technologies that have played a significant role are Tika, Regex, FastAPI, etc.
Implementation Process:
- Customized solutions and assessments
Integrating and collaborating with the software provider to fulfill the company’s recruitment needs helps the HR team to work effortlessly on the hiring process. Customizing ATS and resume parsing functionalities effectively.
- Implementing High-level Architecture
Making the procedure easy for the HR team to parse the resumes, applicants are said to upload their resume or CV in PDF or docx format through which the file is saved locally. Further, the texts from the file are extracted and processed through NLP through spaCy. Then, Utilizing NER from spaCy, the information from the text is extricated. After this process, the extracted information is stored in the MongoDB database.
- Embracing Accuracy
Transforming a set of resumes consisting of different formats(PDF and Docx) to text and annotating the resume text using the NER annotator will aid in bringing more accuracy and reliability to the procedure.
- Training
Through constructive training sessions, employees get familiar with altering the traditional ways and adapting to the new systems. During the integration of the new system, migrating the existing data and composing it to ATS to meet the needs of the company’s recruitment workflows.
- Testing Process
Undergoing meticulous testing, the company can assure the accuracy and efficacy of the parsing and data extraction. Considering the valuable feedback from the HR professionals allows us to make further improvements to maximize the performance and productivity of the system.
Results:
Improved Efficiency
Best for reducing manual effort and time, it helped the company in parsing and screening resumes. In this way, the HR team could focus on other crucial tasks.
Quick to hire
The automated screening process helps save time in screening the resumes and helps in the hiring cycle by shortlisting 50% of candidates.
Qualitative Hiring
Extracting and analyzing the data from the resumes will help discover candidates with high potential and qualifications. It will aid in shortlisting candidates that align precisely with the job requirements and company needs, improving the quality of the hiring process.
This project of ATS has a positive impact on the companies by achieving efficient resume parsing and utilizing NLP techniques. Extracting vital information such as the candidate’s name, phone number, email, education details, professional background, and a comprehensive array of skills improves the screening and hiring quality.
Along with saving time, it reduces manual effort, offers enhanced candidate insights, and adapts to various document formats.
