The war for talent has continued to grow across industries. However, the existing hiring process is flawed and outdated. Most companies still rely on traditional resume screens to identify candidates and recruit new employees; however, this evaluation does not reliably predict an individual’s future fit with a job or company. Instead, researchers suggest that successful placement depends on a candidate’s “fittingness” and would require hiring managers to take into account other off-resume factors.1 With the average job opening attracting 250 resumes, this requires a large time investment for HR teams to review and filter.2 In order to adapt to this new environment, some companies are reevaluating the way they identify and attract candidates.
MACHINE LEARNING IN RECRUITING
One way companies are disrupting the traditional recruiting process is through the use of data and predictive analytics. Pymetrics is a machine learning company that uses data to evaluate and recommend candidates based on their proprietary algorithms. This is a new model for job applicants. Instead of filling out the traditional resume and demographic profiles that are often included in standard job applications. Individuals play online neuroscience games that may remind you more of playing Nintendo than applying for a job. These games have all been designed using neuroscience research to reveal underlying capabilities of the test taker.
In one challenge, the user is asked to select the “missing piece” in geometric patterned puzzles. After approximately 5 min of “testing” a user receives a high-level indication on what skill was tested and how she fared. In this case, the game was measuring Pattern Recognition. In each game, pymetrics measures an individual’s behavior and collects valuable data on each user. After completing a set of 12 required games, test takers receive a personalized report on their capabilities. They also receive job matches based on pymetrics’ recommendation algorithm that uses technology similar to Netflix, Amazon, or Pandora.3 In some cases, a user might even find they are a good fit for one of pymetrics’ partner employers and have a jump-start on the application process.
By using data science and machine learning, pymetrics is able to capture nonlinear relationships between behaviors and predict success in future job functions and companies. Pymetrics is able to provide value to employers by creating algorithms that will specifically predict an individual’s fit and future success in role. From this platform, employers gain access to a broad talent pool that has already been assessed and vetted as a potential match. From the user perspective, individuals gain free skill assessment based on neuroscience games and access to high potential job matches. Overall this platform strategy utilizes data and predictive analytics to streamline the cumbersome recruiting process.
IMPLICATIONS FOR DIVERSITY AND INCLUSION
Companies who work with pymetrics to utilize machine learning in their hiring process can significantly reduce the time that HR teams spend screening resumes and identifying candidates. In addition, this process helps to open up opportunities to individuals who come from less traditional backgrounds. Similarly, this process can help to promote gender and ethnic diversity by removing human bias. By focusing on skill-based data, pymetrics is able to level the playing field in order to open opportunities to a broader set of potential candidates.4
POTENTIAL RISKS AND WATCH OUTS
While machine learning can help to streamline the recruiting process, there are also potential risks. Just as every human makes mistakes, algorithms are imperfect and error prone. Recently, Amazon backtracked on their own experiment with machine learning and hiring after discovering inadvertent gender bias.5
As companies, like pymetrics, design new models for hiring it is important to continue to be thoughtful about the way algorithms are written and what data is used. Algorithms – like people – have the potential for bias, which makes it ever more important to take a critical eye to the methodology and approach to the way they are designed.6
We must also consider which aspects of the hiring process may be missed by an algorithm. For instance, can data truly understand the underlying intentions of a job applicant or adequately measure potential interest? By better understanding these limitations companies can better integrate data-driven processes with human interactions in order to successfully identify and hire the best talent for their organization.
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 Tse, Terence, Mark Esposito, and Olaf Groth. 2018. “Resumes Are Messing Up Hiring”. Harvard Business Review. https://hbr.org/2014/07/resumes-are-messing-up-hiring.
 Gladstone, Jennifer. 2017. “60 Hiring Statistics You Need To Know For 2017”. Ebiinc.Com. https://www.ebiinc.com/resources/blog/hiring-statistics.
 “pymetrics: Using Neuroscience and Data Science to Revolutionize Talent Management”. https://pymetrics.com.
 Gupta, Nidhi. 2018. “Three Ways Machine Learning Is Improving The Hiring Process”. Forbes. https://www.forbes.com/sites/forbestechcouncil/2018/03/26/three-ways-machine-learning-is-improving-the-hiring-process/#43dbcad90e8b.
 “Amazon Scraps A Secret A.I. Recruiting Tool That Didn’t Like Women”. 2018. CNBC. https://www.cnbc.com/2018/10/10/amazon-scraps-a-secret-ai-recruiting-tool-that-showed-bias-against-women.html.
 Tugend, Alina. 2018. “The Commonality Of A.I. And Diversity”. Nytimes.Com. https://www.nytimes.com/2018/11/06/business/dealbook/the-commonality-of-ai-and-diversity.html.