Client Objective
To streamline the recruitment process by automating the classification of incoming resumes based on skills, experience, and job relevance.
Solution
We developed an AI-driven resume classification system that automatically categorizes resumes into predefined job roles and skill buckets.
- Leveraged Machine Learning models trained on a dataset of sample resumes.
- Used Natural Language Processing (NLP) techniques to extract key information such as education, experience, skills, and job titles.
- Implemented a feedback loop to continuously improve classification accuracy over time.
Outcome
- Reduced manual resume sorting time by over 70%.
- Improved recruiter efficiency by ensuring better candidate-job matching.
- System accuracy improved through continuous learning as more resumes were processed.
Technologies Used
- Python
- Scikit-learn
- NLP (spaCy, NLTK)
- Custom ML pipeline for classification
Note: This project was delivered several years ago, showcasing our early capabilities in applied AI and machine learning in HRTech.