Moneyballing Recruiting: A Data-Driven Approach to Battling Bottlenecks and Biases in Hiring

Abstract

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. In this talk, I will discuss how hiring heuristics impact fairness and efficiency using three behavioral studies that I recently conducted and my analyses of over 10 million jobs and their outcomes.