There is another solution we built from scratch for a high-tech start-up — an innovative job board for Asian market. This job board must serve the healthcare and medical science industry and is oriented at top-tier researchers and scientists. There was a dual challenge — both the employers and the applicants cannot describe their skills precisely (either because they cannot lift the NDA or the project descriptions involve highly specific terminology that does not have a commonly accepted meaning yet). Therefore, the job board should be able to understand if the applicant’s skills are relevant to the job posting requirements and also recommend the applicants based on additional parameters. even if there is no exact match of their skills with the job requirements.
We have developed a fully-functional back-end prototype and are currently working on a GUI. The job board works the same as some other popular job portals — the applicant remains anonymous until they privately provide their contacts for the interview, so their current employers have no way of knowing their personnel is actively seeking for a better position. Thus said, our prototype addresses two main concerns of all job boards:
The prototype deploys Word2vec system — a natural language processing algorithm that analyzes both the job postings and the applicants’ CV’s with the help of PoS (Part of Speech) tagging. This helps find correlations between the job requirements and the applicant’s skill summary. The process is augmented with our own algorithms and tuning. Reinforced learning of the ML model happens at the moment of Word2vec suggestion, so each successful skill match or addition of a new skill to the base improves the accuracy of suggestions.
This solution provides the following benefits:
- drastically reduces the time and money expenses on applicant search
- augments the applicant screening to highlight the most relevant applicants
- shortens the recruiter’s overhead
- allows augmenting the recruiter’s soft skills with the platform’s features.