Customer
United Nations
Products and Services
Python
Tensorflow
React JS
Java Microservices
Industry
International Affairs
Organization Size
Corporate (10,001+ employees)
Country
USA
The United Nations is an international organization made up of 193 Member States committed to maintaining international peace and security. Every day the UN works to tackle global challenges and deliver results for those most in need.
Improving Resume Screening Process and Maximizing Accuracy
Being an international organization, vacancy for any post attracts volumes of resumes. This makes it difficult for the human resource department to screen through every resume, which takes several months to process. On top of this, accuracy was a big concern.
We at Hubino offered an AI based solution which uses computer vision to screen through volumes of resumes criteria wise and using deep learning techniques, the screening engine parses every resume and categorizes it.
Easily Define Criteria
It plays an important role in resume analysing. It’s a standard against which you will measure all candidates to determine whether they have the qualifications to perform the job. Selection criteria are developed from the knowledge, skills, and abilities identified in the job analysis and stated in the job description.
The solution as the ability to define criteria in the system like eligibility criteria, technologies, number of years of experience, Industries served, domain expertise and etc.
AI based Screening
Being an international organization, vacancy for any post attracts volumes of resumes. This makes it difficult for the human resource department to screen through every resume, which takes several months to process.
The solution uses computer vision to screen through volumes of resumes which does not require human intelligence. Using deep learning techniques, the screening engine parses every resume and categorizes it. It also reduces time in screening the resume to make quick decisions.
Automatic Categorization after Screening
The categorization of job applications submitted in form of Resumes against available vacancy(s) takes significant time and efforts of an employer. Thus, Resume Classification System (RCS) using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process.
Now categorization of resume takes place automatically after the screening process in this Solution. It minimizes the workload and overcomes human error ultimately leading to more accuracy.
Fully Automated
Recruitment automation enables recruiters to work more efficiently. Tools and platforms streamline menial tasks across all stages of the hiring process, allowing teams to focus their efforts on assessing a candidate’s true fit for the company and role.
This solution helps to automate the screening of resume and categorizing it while defining the criteria. Time taken in the screening of resumes reduced drastically because of its ability to screen them precisely matching the requirements. Increased accuracy in screening resumes through semantic search. This can search based on underlying content in the resume which alleviates the margin for human error.
