One reason skills-based hiring has been slow to take hold is that employers, particularly in tech, don’t always understand how to put it into practice. Here are three ways to get started.

Kelcey Reed, Mohan Reddy

October 19, 2022

5 Min Read

As employers grapple with the impact of an ongoing "Great Reshuffle" and accelerating momentum for educational pathways outside of college, skills-based approaches to hiring are becoming increasingly popular.

Recent research indicates that a growing number of employers are removing college degree requirements from job posti​​ngs, especially in IT and managerial roles, and embracing other methods of determining job readiness. Now, state leaders are following suit, with Maryland Governor Larry Hogan’s recent decision to remove the college degree requirement for thousands of public-sector jobs, and to intentionally focus on hiring workers who are Skilled Through Alternative Routes (STARs).

But the bachelor’s degree still has a powerful hold on many employers’ hiring practices. As many as 19 million STARs remain locked out of job opportunities because of the “paper ceiling” -- the invisible barrier that comes at every turn for workers without a bachelor's degree – despite having the skills to succeed in those jobs. Even some of the most vocal employers that are supportive of new approaches to hiring still have a long way to go when it comes to prioritizing skills in their own recruitment processes.

What’s the reason for this reticence to adopt skills-based hiring? Our experience suggests that while there’s a lot of enthusiasm for the model in theory, it’s not easy to translate into practice. Based on our work building a hiring platform that uses skills-based methods to facilitate hiring STARs, and here are three considerations for businesses that are excited about the potential of skills-based hiring, but not sure how to make the leap.

1. Identify where technology can help -- and where it can’t

Sifting through hundreds of candidates is already time-consuming enough for a hiring manager. Reviewing dozens of individual skills to match them to a job would overwhelm anyone. The good news is that artificial intelligence (AI) and other emerging technologies can do the hard parts for us. However, this still requires finding the right balance – identifying exactly which of those hard parts should be handled by technology, and which require a human element.

AI tools can now analyze massive amounts of labor market data to help understand, for example, how a given occupation can map to a skill set. But the best approaches to skills-based hiring are intentional about how they’re using technology; it works best when supported by human experts. Use analytical tools to be more efficient with tedious tasks, like identifying the right skills and surfacing qualified candidates. However, don’t let them replace people or become an automatic filtering mechanism. Hiring managers and talent leaders must be trained on the need for skills-based hiring and on how to thoughtfully incorporate it into their candidate evaluations.

2. Know what’s in your algorithms and watch out for bias

Using technology for skills-based hiring can take some of the burden off hiring managers by doing the hard work of matching the skills of candidates to those required for roles. Stellarworx, the STARs hiring platform we’ve worked together to develop, captures trillions of unique skill combinations from a variety of real-world sources across industries and experience levels. It then uses machine learning to identify which skills historically led to the most successful job transitions. This helps hiring managers and jobseekers understand the skill requirements of a given role more completely than a traditional job description.

With any AI-driven process there is the risk of bias -- a risk more acute in an industry with a history of discriminatory practices. Many skills-based hiring algorithms use historical job transitions as a data source. But, that data often reflects employers’ degree preferences, so it’s easy for algorithms to perpetuate the very disparities that skills-based hiring is trying to address. The best way to attack this challenge is a combination of data practices and people practices. Skills-based hiring algorithms should leave out protected characteristics like age, race, ethnicity, and gender, which are known to introduce bias. But any data without context can lead to bias, so be sure to apply both mathematical analysis and human judgment to evaluate the model for bias.

3. Set explicit goals and measure success

Two factors can spell doom for any technology investment: They are a lack of clarity around what the investment should yield, and a failure to remove barriers and empower people to make use of the technology.

To see your investment in skills-based hiring pay off requires many groups working together, top to bottom. It takes executive leadership to set the vision, to incentivize progress toward milestones and hold the organization accountable, and most importantly, to give explicit permission to hire based on skills instead of just degrees. It also requires engaging practitioners to refine and tailor job descriptions to focus on specific skills -- leaning on technology to establish a baseline and fill gaps where appropriate. With that level of support, talent leaders can then intentionally screen in STARs and look for specific skills among their pools of applicants.

When employers and workers alike put these ideas into practice, the impact can be transformative. Consider the case of Thomas, a STAR who spent nearly two decades in plumbing before using Stellarworx to find a job as an IT Support Coordinator at a Los Angeles-based entertainment company. Because the platform recognized the communication and collaboration skills he’d developed on the job, along with the coding skills he’d gained from a tech bootcamp, he found a job that suited both his interests and his abilities.

Emerging technologies are making skills-based hiring, once a pie-in-the-sky policy idea, into a reality for employers and workers alike. While questions about both the technology and the methodology -- from confusion about how it works, to concern about the risk of discrimination -- will always come up, the good news is that we now have the experience and evidence to answer them. We hope those answers can help more employers take the steps toward implementing this promising practice -- and building a stronger and more equitable labor market in the process.

About the Author(s)

Kelcey Reed

Chief Technology Officer, Opportunity@Work

Kelcey Reed is an Engineering and IT Executive with 20+ years of success building and delivering next-generation solutions and optimizing technical environments in the FinTech and EdTech industries. He currently serves as Chief Technology Officer at Opportunity@Work, a nonprofit organization working to remove barriers for workers who are skilled through alternative routes other than a bachelor’s degree.

Mohan Reddy

Chief Technology Officer, SkyHive

Mohan Reddy is the CTO of the AI technology company SkyHive, where he oversees all aspects of developing and delivering SkyHive’s technology capabilities and innovation. He previously served as CTO of The Hive in Palo Alto, where he co-founded many startups. A machine learning expert by training, in his 25 years of career Mohan has worked at multiple companies including Zynga, IBM, Schlumberger, Expedia/Travelocity/Sabre, etc., where he architected and built platforms to scale to hundreds of millions of users. He currently also serves as Associate Director at Stanford Human Perception Lab.

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