Shaker Uses Machine Learning to Design the Next Generation of Assessment

Shaker, the market-leading provider of pre-employment assessments, will showcase its award-winning assessment solution at the Gartner ReimagineHR 2018 conference, October 28–30.

Shaker will on hand to talk about their Virtual Job Tryout technology, designed to deliver a multimethod, multimedia evaluation experience that reports on essential performance drivers for on-the-job success, such as overall success, career stability, and role-specific measures.

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Shaker has been incorporating machine learning into its award-winning solution for over a decade and designs assessments that are shorter and more realistic, allowing candidates to respond in their own words and demonstrate a broad set of hard and soft skills that employers need.

“The holy grail of job simulations though has always been unstructured responses,” said Dr. Eric Sydell, Shaker’s EVP of R&D. “Traditional analysis and scoring techniques have not been able to adequately make sense of free-form responses that candidates might speak or type.”

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“At Shaker, our data science team has discovered ways to apply deep learning techniques to new unstructured Virtual Job Tryout exercises,” he added. “Our data scientists are confident these algorithms will soon be able to predict job performance better than human experts—and significantly better than traditional structured-response assessments.”

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