Intelligent Candidate Matching
NLP-based semantic matching model for resumes and job descriptions to automate initial screening.
Challenge
A major financial company was processing thousands of resumes monthly. The HR department spent up to 70% of their working hours on initial screening -- manually matching candidate skills against job requirements. Subjectivity in assessment and recruiter fatigue led to relevant candidates being overlooked. The client sought to automate the initial selection process while maintaining evaluation quality.
Solution
We developed an NLP-based semantic matching model that analyzes resume text and job descriptions across multiple parameters: skills, experience, education, and industry specifics. The system builds vector representations of documents and evaluates the degree of match. Candidates are ranked by relevance with justification -- recruiters can see which parameters triggered a match. The model is continuously fine-tuned on HR specialists' decisions, improving accuracy over time.
Results
Technologies
Approach
Recruitment process analysis
Examining existing data, resume formats and job descriptions, identifying evaluation criteria.
Semantic matching model development
Building the NLP model architecture for vector representation and document matching.
Training on historical hiring data
The model was trained on HR specialists' decisions for precise calibration of matching criteria.
Integration with the client's HR system
Seamless connection to the existing recruitment infrastructure and production deployment.
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