This SBIR Phase II project intends to bring transparency and efficiency to the recruiting industry through a dynamic sourcing and analytics platform. Currently, it is exceedingly difficult to identify ideal candidates when recruiting for a position requiring exact criteria. The proposed platform will enable recruiters to rapidly identify optimal candidates and understand where these candidates are geographically concentrated, working, and being educated. This data-driven transparency will improve recruiter/hiring manager interactions and allow for positions to be filled more rapidly, with less productivity lost by employee turnover. The platform will highlight the most qualified candidates for a position, regardless of preconceived bias, to help uncover overlooked candidates. These efficiencies will benefit recruiters, hiring companies, individual candidates, universities, and society. Given the size of the industry ($160 billion) and the scale of inefficiencies, the project has vast commercial impact potential.
Phase II research and development will be primarily focused around machine learning techniques, leveraged alongside a powerful computing framework, and applied to a substantial dataset containing billions of data points. This technology will be used to drive real-time dynamic analysis resulting in powerful recruiting analytics and ideal job candidates via interactive dashboards. Phase II will build upon the progress achieved in Phase I in the areas of machine-learning classification systems, parallel computing environments to process large quantities of data in real-time, and user-friendly visualizations. The goals of Phase II include increasing the processing power of the data architecture, modeling imputed attributes, improving the accuracy of modeling algorithms, and increasing overall interface performance.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
