SBIR Phase I: Matching Algorithms and Talent Acquisition System to Improve Start-Up Staffing

这个小企业创新研究（SBIR）第一阶段项目旨在为人才获取系统创建核心算法，以编程方式将候选人简历与创业工作机会相匹配。创业公司的招聘需求是独特的，市场缺乏一个有效的平台来加速和提高公司建设的核心竞争力。在简历数据库中进行一般搜索并不能充分捕捉到创业公司招聘的独特匹配要求，也不能返回可接受的结果。本研究旨在结合(a)有限的雇主搜索标准输入，使用一个简单的界面；(b)广泛的规范化输入，每个输入都为创业公司的适合度打分，创建一个自调整算法，用于搜索、发现和配对候选人，以满足创业公司的独特需求。这种方法的创新之处在于，它创建了一个固有的系统，既重视创业公司的工作/生活的硬属性，也重视软属性。如果成功的话，这一努力将为雇主指出那些最有可能在这些机会中脱颖而出的人，从而消除很多猜测。数据提取、评分技术和贝叶斯过滤将应用于简历、问卷、求职历史、社交网络地图、候选人推荐和搜索词，以提供算法。这个项目更广泛的影响将是通过加速和改善组织中各级强大团队的人员配置来提高年轻公司的成功率。该公司认为，以创业公司为中心的职业资源在美国在线招聘行业具有巨大的商业潜力。竞争性招聘方法将初创公司招聘视为与大公司招聘相同，但经验表明，围绕这个社区的独特需求建立的方法有巨大的需求。公司受益于(a)专注于自我选择进入这个生态系统的人才，以及(b)使用特定于创业公司的成功标准对这些候选人进行算法过滤。这项研究将创建第一个专门针对初创企业的平台，这是雇主们一再要求的。拟议的系统将提供有利于新兴成长型公司需求的质量和速度。它还将提供创业公司创造就业机会的重要指标，这是衡量私营公司增长的最佳指标。初创公司生态系统中的服务提供商将为识别快速增长的公司的数据付费，这创造了额外的收入机会。

This Small Business Innovation Research (SBIR) Phase I project aims to create core algorithms for a Talent Acquisition System to programmatically match candidate resumes to startup job opportunities. Startup hiring needs are unique, and the market lacks an effective platform to accelerate and improve this core competency for company building. Generic search of a resume database does not sufficiently capture the unique fit requirements of startup employment nor return acceptable results. This research aims to incorporate (a) limited employer input of search criteria using a simple interface with (b) a broad range of normalized inputs, each individually scored for startup fit, to create a self-tuning algorithm for the search, discovery, and pairing of candidates to the unique needs of startups. The innovation in this approach is to create a system inherently weighted to both the hard and soft attributes of startup work/life. If successful, this effort will remove much of the guesswork by pointing employers to those most likely to excel in these opportunities. Data extraction, scoring techniques, and Bayesian filtering will be applied to resumes, questionnaires, job search histories, social networking maps, candidate referrals, and search terms to feed the algorithm.The broader impact of this project will be to improve the success rate for young companies by accelerating and improving the staffing of strong teams at every level in the organization. The company believes there is significant commercial potential for a startup centric career resource in the U.S. online recruitment industry. Competitive approaches treat startup recruiting as identical to large company recruiting, yet experience indicates there is tremendous demand for an approach built around the unique needs of this community. Companies benefit by (a) focusing on talent which self-selects into this ecosystem and (b) algorithmically filtering these candidates using startup-specific success criteria. This research will create the first platform of its kind specific to startups, something employers have repeatedly requested. The proposed system will deliver both quality and speed biased to the needs of emerging growth companies. It will also provide important metrics on startup job creation which form the best available proxy for private company growth. Service providers in the startup ecosystem will pay for data identifying fast growing companies, and this creates an additional revenue opportunity.