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353 – SPEED: Statistical Learning and Data Science

Predicting Job Application Success with Two-Stage, Bayesian Modeling of Features Extracted from Candidate-Role Pairs

Sponsor: Section on Statistical Learning and Data Science
Keywords: high-dimensional data, structured features, unstructured features, natural language processing

Jon Krohn

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Gabe Rives-Corbett

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Ed Donner

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We describe a two-stage model for identifying the best-suited candidates for a given role that (1) learns hiring manager preferences for the role; (2) can be updated frequently, with low latency; and (3) scales to a very large number of roles. In the first stage, dozens of candidate-role features are modeled in a regression across all applications and roles. The feature weights are subsequently fed as priors into n individual Bayesian models representing n roles. Cross-validated results indicate this approach improves classification accuracy, with the area under the curve of the receiver operating characteristic improving from 71.8% to 77.0%.

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