The goal of this project is to streamline the hiring process for technology companies by leveraging a Machine Learning (ML) Natural Language Processing (NLP) pipeline. This facilitates the matching process with the specific needs of recruiters.
The primary objective of this project is to automatically highlight the best matching candidates based on how fit these candidates are for a given role. Through Natural Language Processing (NLP) and Machine Learning, the pipeline aims to match the most suitable candidates to specific roles based on their LinkedIn information.
The dataset is a spreadsheet that summarizes the candidates information in a fixed number of columns. Each column is a specific candidate feature whose entry can be either a sequence of words or a number.
The pipeline conducts searches based on role-specific keywords, such as "full-stack software engineer" or "Aspiring human resources." These keywords may vary depending on the specific role requirements.
The ML pipeline screens job candidates based on their LinkedIn information. NLP capabilities, such as NLTK and SBERT Sentence Transformers, are leveraged to provide semantic embeddings for candidate descriptions. The candidates are then ranked based on how well their information matches to the role keywords.
The pipeline is equipped with a continuous learning mechanism that utilizes manual supervisory signals. Recruiters can provide feedback by starring candidates, which is then used to update the ranking algorithm. This feedback loop ensures ongoing improvement and consistent delivery of better results over time.
To utilize this Hiring Pipeline, follow these steps:
- Define role-specific keywords based on the requirements of the position.
- Acquire candidate descriptions from LinkedIn or other relevant sources.
- Run the algorithm to extract semantic embeddings from candidate descriptions.
- Use the pipeline to screen and rank the candidates based on the extracted embeddings and specific recruiter needs.
- Provide manual supervisory signals (starring candidates) to improve the ranking algorithm over time.
This ML pipeline optimizes the talent acquisition process for technology companies by automating candidate screening and ranking. By leveraging NLP capabilities and continuous learning mechanisms, the pipeline significantly reduces costs and streamlines the hiring process. The automated screening and ranking of candidates based on their LinkedIn information ensures that the most suitable candidates are identified for specific roles. With ongoing improvements and feedback from recruiters, the pipeline consistently delivers better results and enhances the overall efficiency of the talent acquisition process, resulting in reduced costs and improved efficiency.