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SBIR Phase I: A Big Data Skills-To-Tasks Ontology For Career Mapping, Job Matching, And Talent Acquisition.

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1846420
Agency Tracking Number: 1846420
Amount: $214,218.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: IT
Solicitation Number: N/A
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-02-01
Award End Date (Contract End Date): 2020-01-31
Small Business Information
RESTON, VA 20191
United States
DUNS: 081153689
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Lokesh Dani
 (571) 421-7979
Business Contact
 Lokesh Dani
Phone: (571) 421-7979
Research Institution

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to make talent search faster and much more effective. More specifically, it will aim to make it easier to connect open jobs with the talents of job seekers. The innovation proposed here will redefine job search in a time when the labor market is in major flux, and the technology developed in this project will enable job searching to move far beyond a one-page resume and focus more on the skills needed to succeed in a job. In so doing, the innovation will enable job seekers to find new opportunities that are aligned with their career interests and enable employers to find skilled talent matched to their project needs. Employers using this technology will be able to search for talent according to what they need performed, without necessarily knowing how the task will be performed. Similarly, job seekers will be able to search for jobs that they may not have known existed but are still strongly aligned with their career goals. This Small Business Innovation Research (SBIR) Phase I project will develop a skills-to-tasks matching system that serves as a basis for which worker skills are employed to perform which tasks on the job. Currently, available data on the skill content of occupations does not capture variation across industries, geographies, or over time. Nor does it capture the applicability of a worker's current skills to new workplace tasks, an important consideration for up-skilling and re-training programs. This limitation has left a major source of information asymmetry intact in the 21st century job market, where the most effective talent search strategy remains peer-referrals. This same lack of granularity in available data has limited the effectiveness of policymakers to measure the skills-gap, identify career pathways, and design training programs most needed in their local economies. The research proposed in this project will produce an up-to-date compendium of the skills and work activities that are evolving in the labor market and map the relationships between them. This technology will provide a dynamic and granular view of the labor market and improve the quality of information available to decision-makers in industry, education, workforce development, as well as for the study of the future of work. 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.

* Information listed above is at the time of submission. *

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