All Technologies Used
Motivation
The customer needed a custom solution to automatically collect resumes from various websites, create a database, classify candidates, enhance their CVs with missing skills, and enable efficient search across the candidate database. The goal was to improve the hiring process, reducing time and costs for recruitment while ensuring high-quality matches between candidates and job descriptions.
Main Challenges
The main challenge was extracting unstructured data from multiple job sites like LinkedIn, Indeed, and Stack Overflow, where candidates often upload incomplete or outdated resumes. The system needed to merge this data into a comprehensive candidate profile, avoiding web scraping limitations imposed by these sites.
Recruiters lack specific knowledge of the vast array of technologies, programming languages, and frameworks that developers use. The challenge was to train a machine learning model to build relationships between these technologies and predict missing skills based on the available resume data.
Key Features
- Web Scraping Engine: The platform automatically scrapes resumes from various job sites and merges them into a comprehensive candidate profile.
- Data Classification and Tagging: Resumes are classified and tagged with relevant skills, programming languages, and frameworks, improving search accuracy.
- Machine Learning Model: The system predicts missing skills and enhances resumes by associating known technologies with similar competencies.
- Cloud-Based Architecture: The solution is scalable and cost-effective, hosted in the cloud to avoid on-site infrastructure and provide flexibility.
- Proxy Management System: A built-in proxy management system ensures that the platform bypasses scraping limitations imposed by job sites.
Our Approach
Project Impact
The platform processes half a million webpages monthly, providing recruiters with accurate and enriched candidate profiles. This solution speeds up the hiring process, reduces stress for recruiters, and improves candidate matching. The system processes an average of 17,000 webpages per day and increases the number of relevant candidates by 127%. On average, it only takes 4 seconds to classify and tag a candidate, making the process significantly more efficient.