Choosing the right person for a job can be challenging. The sheer number of resumes can be overwhelming. But even for organizations patient enough to review each application, poor choices can arise from psychological biases ranging from racial discrimination to narrow bracketing (in which people overemphasize subsets — rather than the universe — of choices, for example, choosing the best candidate interviewed that day rather than the best candidate interviewed over the course of the search). For tech-savvy organizations, recent applications of machine learning coupled with increased access to data raise the possibility of improving hiring decisions with the help of algorithms.