The client wanted to extract data from resumes, match it with Job Description and Rank the resumes on the basis of skill-sets in order to shorten the Hiring LifeCycle and reduce the time and efforts needed for the first level interview.

We used the Computer Vision Techniques with a layer of Machine Learning to extract text from PDF resumes. The Text similarity matching and Siamese algorithms helped in matching requirements with resumes. Our team of data scientists worked on creating a matching engine to match Job description with Resumes and rank the resume to identify the top 5% of the applications based on experience, education, certifications, skillsets etc.

This helped in improving the hiring pipeline with direct saving of 2 million USD over 6 months and shorten the hiring life cycle with improved time for first interview by 73% and impact on hiring life cycle to be 38% leading to significant cost saving.