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.
The project created core algorithms for a Talent Acquisition System to pragmatically match candidates 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 incorporates (a) limited employer input of search criteria using a Google-like 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 creates a system inherently weighted to both the hard and soft attributes of startup work/life. Startup employment is not for everyone. 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 full text search are applied to resumes, questionnaires, job search histories, social networking maps and search terms to feed the algorithm. In Phase 1 we confirmed the feasibility of programmatically matching candidates to startup job opportunities. During the project the following information was collected and used to effectively search for candidates through a simple user interface. Data provided directly by candidate Resume/CV User profile (work experience, education, interests etc) Attitudinal questionnaire Data gathered about candidates Candidate job search histories Job posts viewed by candidates Jobs "applied to" by candidates Social media connections Data provided by companies Stage, size, industry, funding Other rival or respected companies Of course, the value of the algorithm in the marketplace is determined by the companies using the system. With this in mind, our efforts during Phase 1 have been decidedly pragmatic focusing on building out the capability to capture, process, display, analyze, and search candidate information. In January 2011 the beta version of the Talent Vault was launched. Broader Impacts 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. StartUpHire believes there is significant commercial potential for a startup centric career resource in the $6 billion annual 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 has created the first platform of its kind specific to startups, something employers have repeatedly requested.
