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Given a huge set of applicants, how should a firm allocate sequential resumescreenings, phone interviews, and in-person site visits? In a tiered interviewprocess, later stages (e.g., in-person visits) are more informative, but alsomore expensive than earlier stages (e.g., resume screenings). Using acceptedhiring models and the concept of structured interviews, a best practice inhuman resources, we cast tiered hiring as a combinatorial pure exploration(CPE) problem in the stochastic multi-armed bandit setting. The goal is toselect a subset of arms (in our case, applicants) with some combinatorialstructure. We present new algorithms in both the probably approximately correct(PAC) and fixed-budget settings that select a near-optimal cohort with provableguarantees. We show via simulations on real data from one of the largestUS-based computer science graduate programs that our algorithms make betterhiring decisions or use less budget than the status quo.

Making the Cut: A Bandit-based Approach to Tiered Interviewing