The Stated and Hidden Expectations: Applying Natural Language Processing Techniques to Understand Postdoctoral Job Postings

Abstract

This paper represents recent work applying natural language processing (NLP) techniques to generate insights on postdoc experiences from the job postings in engineering and computer science (CS). Postdoctoral positions are one of the important components of the academic career pipeline. It offers significant educational and professional opportunities, however, limited research has been focused on postdocs, especially in the field of engineering and CS with significant gender disparities in postdoc and faculty positions. In this work, we explore NLP techniques to analyze the job postings for recruiting engineering and CS postdocs in the U.S. We utilized a Knowledge, Skills, and Attributes (KSAs) framework to characterize the KSAs as the stated expectations noted in job postings. Our results revealed that communication skills, academic writing, and computational proficiency are most frequently required KSAs. We also discovered that postdoctoral scholar postings are vague, often lacking stated specific skills necessary for the position which may discourage potential applicants. By applying the lexicon-based gender coding method on job postings, we disclosed that the majority of the postdoc job postings tend to use gendered language. Our findings indicated that it is important to neutralize the masculine language used in job postings, to explicitly include more KSAs, and to clearly state expectations for positions to encourage underrepresented populations' participation and to support sustainable academic career development for postdocs in engineering and CS.

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