Would you believe that your ability to hire a data scientist could be debilitated by altering just 10 innocuous words in a job description? That changing 10 words in seemingly harmless ways can result in a job taking weeks longer to fill and tens of thousands of dollars wasted?
Companies approach hiring and talent management as an art, relying on judgment and experience when conceptualizing jobs, drafting JDs, and screening and assessing candidates. With recent advances in NLP, data science and decision science, we have the ability to interrogate these “common sense” judgments to see if they help or hurt hiring teams in competitive talent markets, especially for technology and data science roles.
Maryam Jahanshahi actively studies these hiring heuristics. Join her as she showcases three behavioral studies she has recently conducted and her analyses of over 10 million jobs and their outcomes.
Topics include counterintuitive hiring patterns such as:
Maryam Jahanshahi is a research scientist at TapRecruit, a platform that uses AI and automation tools to bring efficiency and fairness to the recruiting process. She holds a PhD from the Icahn School of Medicine at Mount Sinai, where she studied molecular regulators of organ-size control. Maryam’s long-term research goal is to reduce bias in decision making by using a combination of computation linguistics, machine learning, and behavioral economics methods.
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