Development of SaaS Software for Talent Acquisition Using Natural Language Processing and Text Mining Algorithms

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TalentNet, founded in 2013, is a platform dedicated to the contingent labour direct sourcing marketplace. Through TalentNet's leadership, the direct sourcing marketplace has evolved into one of the fastest-growing trends in the contingent labour marketplace. TalentNet is focused on bridging the gap between talent acquisition, contingent labour, the gig economy, and the human cloud. The objective of this project is to develop a SaaS software platform for talent acquisition. This project depends on natural language process (NLP) and text mining algorithms to identify keywords within candidates' resumes and compares them to the keywords within the job posting. In order to successfully develop this SaaS platform, data preparation will have to take place to identify keywords and create new variables. Researchers will work with the TalentNet team to find and test new AI/ML algorithms that will improve the existing software, and identify primary and secondary qualifications for each job opening so that the most qualified candidates are identified from each applicant pool. The successful development of a SaaS Software platform into TalentNet existing platform will allow clients to identify the most suitable candidates for their job openings. It is anticipated that the improved talent acquisition will directly translate to increased employment, and reduced time spent reviewing and interviewing unqualified candidates. In turn, this will lower recruiting costs and increase fill times of positions. This improved software also has the potential to increase both employer and employee job satisfaction. Further, the ability to successfully match suitable candidates to job positions will lead to additional revenue for TalentNet. This will contribute to the company's continued growth and solidify its market leadership in the industry as competitors continue to emerge.