10,000 Job Analyses Per Hour: A Language-Based Approach to Synthesizing KSA and Interest Ratings
Dan Putka, Ph.D.
Human Resources Research Organization (HumRRO)
Can we train a machine to profile jobs? Yes, we can. In this presentation, I'll share results of an innovative project that applied a blend of natural language processing (NLP) and modern prediction methods to job and task descriptions for purposes of quickly synthesizing KSA and interest ratings for an entire population of jobs. This will be a fun, fascinating, and thought-provoking dive into cutting edge work that aims to breathe new life into job analysis science and practice – best of all, it will be equation-free
Dan Putka is a Principal Staff Scientist at HumRRO, past President of PTCMW, and a Fellow of APA and three of its Divisions, to include SIOP. Over the past several years he has been presenting and publishing work that evaluates applications of machine learning in I-O science and practice. Dan received his Ph.D. in I-O psychology with a minor in quantitative psychology from Ohio University.
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