SBIR Phase II: A Dynamic Real-Time Analytics Recruiting Platform

SBIR 第二阶段：动态实时分析招聘平台

• 批准号: 1853200
• 负责人: Carlo Martinez
• 金额: $ 75万
• 依托单位: Steppingblocks, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-15 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1853200&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Dynamic; Real

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.

SBIR二期项目旨在通过一个动态的采购和分析平台，为招聘行业带来透明度和效率。目前，在招聘需要精确标准的职位时，很难找到理想的候选人。提出的平台将使招聘人员能够快速识别最佳候选人，并了解这些候选人在地理上集中，工作和受教育的地方。这种数据驱动的透明度将改善招聘人员/招聘经理的互动，并允许更快地填补职位空缺，减少员工流失造成的生产力损失。该平台将突出最符合职位要求的候选人，而不考虑先入为主的偏见，以帮助发现被忽视的候选人。这些效率将使招聘人员、招聘公司、个人候选人、大学和社会受益。考虑到该行业的规模（1600亿美元）和低效的规模，该项目具有巨大的商业影响潜力。第二阶段的研究和开发将主要集中在机器学习技术上，利用强大的计算框架，并应用于包含数十亿个数据点的大量数据集。这项技术将用于推动实时动态分析，从而通过交互式仪表板产生强大的招聘分析和理想的求职者。第二阶段将建立在第一阶段在机器学习分类系统、并行计算环境以实时处理大量数据和用户友好可视化领域取得的进展的基础上。第二阶段的目标包括提高数据体系结构的处理能力，对输入属性进行建模，提高建模算法的准确性，以及提高整体接口性能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。