| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | April 17, 2019 |
| Latest Amendment Date: | April 17, 2019 |
| Award Number: | 1853200 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Rajesh Mehta
rmehta@nsf.gov (703)292-2174 TI Translational Impacts TIP Dir for Tech, Innovation, & Partnerships |
| Start Date: | April 15, 2019 |
| End Date: | March 31, 2021 (Estimated) |
| Total Intended Award Amount: | $749,999.00 |
| Total Awarded Amount to Date: | $749,999.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
3423 PIEDMONT RD NE ATLANTA GA US 30305-1754 (478)278-7622 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
3423 Piedmont Rd NE Atlanta GA US 30305-1751 |
| Primary Place of Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | SBIR Phase II |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Incumbent recruiting tools are limited in their ability to show detailed talent pool analytics and help companies target ideal candidates with significant granularity. Many hiring decisions are dictated by the arbitrary, and potentially biased, candidate-ranking algorithms of current vendors. Highly qualified candidates, especially those in underrepresented populations, can easily be overlooked without more analytics-based solutions with robust filtering capabilities.
This project set out to build an innovative, analytics-based solution to the current problem. During this project, our team successfully built a novel computing framework capable of processing billions of data points in less than one second. This allows for real-time interactive visualizations that help users understand talent pools faster than previously possible. Additional modeling attributes, made possible by machine learning systems, directly target recruiter problems that were not previously addressed.
The early impacts of this project look promising from a commercial and societal perspective. Commercially, users are now lowering their time to placement due to increased transparency and more appropriate expectations between recruiters and clients. Additional candidates can now be discovered by exact metrics, rather than an arbitrary candidate-ranking algorithm. This not only gives recruiters enhanced selectivity, but also allows typically underrepresented talent to be more easily engaged with and hired. Other use cases are already proving successful as well with startup business development and market research. Our hope is that this new tool can continue to bring transparency and efficiency to society, well beyond our original scope.
Last Modified: 07/07/2021
Modified by: Carlo Martinez
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