Highlighting Gender Bias and Misaligned Expectations in Job Advertisements for Data Professionals

The Future Data Services project, “Optimizing Data Professional Success: Identifying Skills, Career Trajectories, and Training Requirements for Enhanced Data Service Delivery,” aims to improve training and development for data service staff through skill mapping, curriculum design, and equitable recruitment strategies. This paper explores implicit gender bias in job advertisements for data professionals, applying Gaucher, Friesen, and Kay’s (2011) lexical analysis framework to identify gendered language.

Building on the Future Data Services Report (Green, 2024), which analyzed 315 UK job advertisements, the project identified key misalignments between advertised roles and job realities. Person specifications often emphasized technical skills, such as research and data expertise, while interviews with professionals highlighted the importance of soft skills like customer service. Many job descriptions appeared to derive from boilerplate templates, especially in academic institutions, causing mismatches between expectations and actual responsibilities.

This research is contextualized within prior studies, including Thielen and Neeser (2020), which documented the shift toward hiring data professionals outside traditional librarian pipelines in U.S. academic libraries. Additionally, Hu et al. (2024) demonstrated how job advertisement language can reinforce or disrupt gender and racial inequalities in the labour force.

The paper provides a comprehensive analysis of gendered language in job advertisements for data professionals, identifies systemic biases, and offers recommendations to aligning job advertisements with equitable career opportunities in data services.