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Hiring tends to be a high-stakes endeavor, with the average length of the job interview process standing at about 27 days. Seventy-six percent of hiring staff say that attracting quality candidates is their biggest challenge, while candidates say that they’re often dissuaded by jobs with a prolonged screening process. When all is said and done, the average employer spends around $4,129 to fill an open position — a hefty sum to be sure.
Increasingly, vendors are pitching AI as the solution to hiring woes, touting the technology’s supposed superiority at matching candidates with available jobs. Ninety-six percent of HR managers in one survey believed that AI can improve talent acquisition and retention “significantly,” and advocates spotlight benefits like the automation of tedious manual tasks and the elimination of “talent waste.”
HireEZ — one of those vendors — claims to expedite the recruitment process by reconciling candidate data across systems including the open web, customer relationship management software, and lists of imported candidates. Formerly known as Hiretual, the company today announced that it raised $26M in a series B+ funding round led by Conductive Ventures, bringing HireEZ’s total raised to $45.5 million.
Like rival recruitment platforms, HireEZ lets customers set up automatic follow-up messages and uncover candidate contact information like work and personal emails, phone numbers, and social media profiles. It also acts as a single source of truth within a company, making candidates’ information searchable and clean.
“Enterprises are facing a growing labor shortage, and efficiently and ethically finding talent is going to be a critical need for companies to stay competitive. But enterprise organizations are typically using dozens of systems, which makes implementing new tools taxing, slow, and can create inefficiencies and blind spots within data and processes. In short, job candidates today have a lot of options, and companies are struggling to get jobs in front of people fast enough,” HireEZ CEO Steven Jiang, one of the cofounders of HireEz, told VentureBeat via email. “HireEZ helps organizations move faster and scale, by intelligently sourcing, engaging, analyzing, and integrating top talent without upending existing enterprise platforms.”
HireEZ’s search functionality uses AI to uncover appropriate candidates from the internet at large. Recruiters provide job titles, skills, locations, and other search factors to focus on, and the platform leverages natural language processing (NLP) to recommend related skills or parse an existing job description. When running a search, HireEZ expands the search to include semantically similar terms that may not be part of the keyword set. For example, searching for an “inside sales rep” will pick up a person with a sales skillset that states their title as “revenue growth superstar.”
To this end, HireEZ’s AI ranks candidates by how well they match the requirements of a job, and then automatically adjusts rankings for the next round of candidates based on user interactions. As with most AI technologies, the system becomes more accurate over time, according to Jiang.
“The HireEZ platform automatically matches candidates’ professional profiles based on the relevancy (job skills, work experience, and other resume information) to what a recruiter, hiring manager — or anyone looking for talent — is looking for,” he added. “Machine learning then samples highly qualified candidate profiles for the user to review, and then further learns from that user’s selected profiles to recommend more similar candidate profiles to the user according to that user’s preference and discretion.”
When Jiang and Xinwen Zhang, both former engineers at Samsung’s mobile division, founded HireEZ, there wasn’t as much competition in the AI recruitment technology space. That changed as investors poured billions into HR tech startups during the pandemic months. Sense, a startup developing AI-powered recruitment and hiring solutions for companies, raised $50 million in December. Another AI recruitment tool, Celential.ai, which focuses on the software engineering market, recently closed a $9.5 million round.
Jiang claims that 261-employee HireEZ’s differentiator is its large library of potential candidates. The company claims to have a database of over 750 million profiles aggregated from over 45 platforms, including social and career websites like LinkedIn, Facebook, Twitter, and Indeed.
“We experienced 2.5 times year-over-year revenue growth in 2021. We serve over 600 enterprise customers and more than 175,000 total users worldwide. [And] currently, HireEZ actively supports 40% of the top 20 companies listed as the world’s most valuable brands,” Jiang said. “Enterprise recruiters urgently need to shift from inbound to outbound recruiting practices or they will most certainly be left behind in this competitive job market. Factors such as the ongoing labor shortage … and heightened expectations from talent have created a candidate-driven market which will require employers to take a more proactive approach to recruiting if they want to grow.”
Of course, it should be noted that some AI recruitment software has been shown to perpetuate the biases that exist in hiring. A 2019 study conducted by Harvard Business Review (HBR) found that it’s easy for algorithms used in the recruiting process to reproduce bias from the real world when not mitigated by a human. More recently, a report from the University of Pennsylvania identified ways that AI-powered recruitment platforms “reflect, recreate, and reinforce anti-Black bias,” including providing job recommendations based on identity rather than qualifications.
The coauthors of the HBR report don’t put the onus on vendors to debias their technologies, necessarily. Rather, they suggest that employers using AI-enabled recruiting tools analyze their entire pipeline in order to “detect places where latent bias lurks or emerges anew.”
When asked about how HireEZ treats potential bias in its candidate-job matching algorithms, Jiang said that the platform’s machine learning technologies are “carefully designed, closely monitored, and consistently improved” to “ensure fairness is inherent” in the matching process.
“The machine learning model is 100% skills-focused to mitigate bias and avoid determining candidate viability based on any other features, such as data on gender, race, age, education, etc.,” Jiang added. “Additionally, the platform allows users to easily apply filters to search for talent from underrepresented groups and aid in equitable hiring practices. HireEZ also maintains a robust AI governance and accountability team, including our CTO, machine learning engineers and consultants, and we bring in other resources to help clients with any needs related to training or education on AI governance.”
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