The hiring process for new labor has a well-deserved reputation for being inconsistent, time-consuming, and non-democratic. Although some companies have made headway into solving this problem with new technological platforms (e.g., Aspiring Minds) they tend to focus primarily on high-skilled labor. As a result, hiring for high-skilled labor has become more seamless and streamlined. In stark contrast, however, there are nearly 12.3 million community college students in the United States who struggle to find employment. Community college graduates are more likely to seek what is known as “middle-skilled labor” and tend to fill roles that require specific skills and certifications, but typically not a four-year degree (e.g., HVAC technician). At the same time, paradoxically, there are more than 3 million unfilled middle-skilled jobs that these community college graduates could ostensibly fill. The inefficiency in this labor market is caused by a misalignment between job-seeking community college students and prospective employers. We believe that there is no effective channel or signaling mechanism community college students can use to convey their skills to prospective employers. A solution that can bridge the gap between students and employers would be a critical step toward optimizing middle-skilled employment and improving the economic fortunes of community college graduates.
We propose an augmented intelligence solution that would sit as a platform between prospective employers and community college students. Traditionally, applicants submit a non-standardized resume that they believe highlights their abilities. Prospective employers then try to match applicant resumes against a nebulous list of criteria they believe are important for the position they are trying to fill. The resulting inefficiency not only means that jobs go unfilled, but it also means that the wrong person may be hired for the wrong role.
In our augmented intelligence solution, we propose that employers use our platform to submit a standard, text-heavy job description. Our platform will translate these job descriptions into simplified skill lists based on a database of similar roles and demonstrated qualifications. This process will require iteration with employers at the outset in order to create a sufficiently large database that can be truly predictive. As these lists are completed, they will be published via the platform as simplified job descriptions reflected in terms of the core skills they require.
On the supply side, community college students create profiles via our platform and fill out all courses they have taken and all relevant work experience. The platform will use a similar database in order to translate disparate coursework and professional work into a list of skills. Students will then be able to access employer job postings and immediately see how well their skills align with employer requirements. Once they apply, employers will not only have access to our platform’s curated list of applicant skills but also traditional application components (e.g., resumes).
We believe that streamlining both sides of this market will enable more efficient hiring and will help close some of the gaps we observe in the middle-skills job market.
Solution Development and Validation
At the outset, we would collect data from prospective employers on some of their commonly filled roles and consult with them to identify the core skills they believe the roles require. On the supply side, we will work with community colleges to translate typical courses into skills (i.e., accounting classes demand a different skill set than a history course).
Model Development / Validation
As the database grows sufficiently large we will begin by conducting a number of small pilots at individual community colleges. Testing the platform in small environments will help us not only validate the efficacy of the model, but will also enable us to adjust the underlying data set in order to make the platform more predictive.
As the model matures, we will expand the pilots to include regions (e.g., Chicagoland) in order to see how combining data from disparate sources changes our model. We ultimately will need to adjust the underlying data set in order to account for differences in how employers articulate skillsets across different parts of the country. A more comprehensive set of pilots will enable us to understand how the data interplays and make these adjustments as necessary
After sufficient testing of the model, we will provide a beta version for a target list of businesses and students to test.
We have defined a number of benchmarks for both employers and students that can help demonstrate our platform’s value. For employers, tracking average time to hire, average tenure of new hires, and management satisfaction ratings can help validate our platform’s effect relative to the base case.
For students, tracking average number of interviews completed, average time to hire, and post-hire satisfaction can help convey our platform’s value.