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After more than 10 career changes and four plus years as a hiring manager, Claire McTaggart, now the CEO of SquarePeg, an intelligent job matching software company, was fed up with all the job search guesswork.
“The idea that with one page of information or a few keyword searches, we can find the right companies to apply to or candidates to interview, is simply not fit for today’s job market,” McTaggart said.
With so many missing puzzle pieces in the online job market, McTaggart knew it was time for a change. And with her experience from both sides of the spectrum, she took it upon herself to answer some of the most difficult questions.
“As a career-switcher, it was hard to stand out based on a resume alone, but how could I communicate some of my best soft skill attributes in a reliable way? And how would I know which companies would be a good fit? — As a hiring manager, it was difficult to identify talent based on fit, so I used degree, GPA, and job history as a filter, even though those parameters are not necessarily predictive of future performance,” she continued.
McTaggart set out to find a solution to the questions and challenges brought on by both sides of the process by launching SquarePeg. Here’s how her team is using a personalized, data-driven solution to make the job search and hiring more effective:
Taking the uncertainty out of the job search
Job searching has become part of a daily routine for many. In fact, according to a 2016 CareerBuilder report, 76 percent of full-time employed workers are either actively looking for or open to new job opportunities.
Unfortunately, the amount of new technology on the job-search front may be making job seekers’ lives more difficult. The same report found, on average, job seekers use 16 total resources in their job search and half of the professionals who responded are wondering if companies are even looking for someone like them. Is it worth it?
Job seeker confidence takes a major hit in this uncertainty. This often impacts how accurately they portray their true personalities and leaves hiring managers with a skewed perception of candidate fit. But SquarePeg fights back against these guessing games to help job seekers and hiring managers solve their search problems.
“SquarePeg uses science-backed assessments to measure job fit across a variety of dimensions (personality fit, environment fit, preferences fit, competency fit), and its smart matching algorithm then connects companies and candidates directly, without the need for search,” McTaggart explained.
Job seekers take SquarePeg’s JobFit assessments once, then receive curated company matches based on their results. Hiring managers also take one 40 minute assessment to set up their profiles, so they’re effectively matched with top candidates.
Putting SquarePeg to the test
One of the most telling ways to test your product is putting it to use yourself. That’s exactly what McTaggart and her team decided to do in the early stages of their HR tech start-up. In their case, finding the right talent wasn’t just their top priority as a company, it’s everything their mission and company goals stand for.
McTaggart said, “When we used our platform for our first two hires, we were able to evaluate the experience from the company and job seeker perspective. That led us to a redesign of the user experience and journey, and helped us notice areas of growth and improvement in our platform that user feedback alone would not have surfaced.”
Now, SquarePeg only practices what they preach by hiring exclusively through their own platform. This is done by getting to know candidates’ traits and competencies through their assessments to align with their hiring priorities. Then, they build a pipeline of interested candidates on an ongoing basis, rather than a one-and-done resume unload.
As SquarePeg’s hiring process progresses, their platform takes note of which candidates they pass on and who they decide to hire. Based on these decisions, it’s predictive capabilities refine future matches so they’re only seeing the best candidates for their openings.
With this deep understanding of the most practical and intuitive application of the platform, the SquarePeg team is excited about helping hiring managers use the same process to find their perfect matches.
“We are connecting companies with top talent they might have otherwise missed or overlooked, with a high degree of efficiency,” said McTaggart.
Identifying top talent in the future
SquarePeg is already making a more effective job search and interview process with the information their analytics provides job seekers and hiring managers. However, “good enough” isn’t a word the SquarePeg team is familiar with. In the future, their plan is to further the capabilities of HR functions to continue to better identify top talent.
McTaggart explained, “This includes team analysis, quarterly reporting to analyze hiring decisions, and machine learning to help increase the quality and efficiency of hiring, the more an employer uses SquarePeg.”
The ultimate goal lies in hiring managers spending less time searching and reviewing candidates, while also being more informed about what candidates are the perfect fit for their team.
On the flip side, SquarePeg is amping up to also help job seekers identify their optimal career path. Their plan is to enhance assessments and personalized reports to actually guide career switchers through the job search process in a new and improved technological way.
- Intelligent job matching algorithm
- Reports on job seekers’ dominant workplace traits with explanations relevant to their careers
- Matches job seekers to careers based on unique traits
- Directly connects job seekers with matching employers
- Employer profiles are created from thorough and diverse assessments
- Source and assess all candidates before they’re sent to hiring managers
- Refined predictive capabilities
Improvements we’d like to see:
- Direct messaging capabilities between job seekers and hiring managers
- System for hiring managers to collaborate with their team
- Ability to make notes and keep track of candidates who didn’t receive an offer
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