Highlighting gender bias and misaligned expectations in job advertisements for data professionals

Green, Elizabeth; Lowe, Hilary

Abstract

Managing human resources in UK data services requires a strategic, evidence-based approach that encompasses recruitment, training, employee development, and equitable reward structures. Increasing emphasis has been placed on embedding equality, diversity, and inclusion (EDI) into workforce practices across public sector data infrastructures. However, the language used in job advertisements may unintentionally undermine these goals. As part of the Future Data Services (FDS) project—Optimizing Data Professional Success: Identifying Skills, Career Trajectories, and Training Requirements for Enhanced Data Service Delivery—this study analysed 300 UK job advertisements for data-related roles to examine patterns in recruitment language, pay, and job location.
Using Gaucher et al.’s (2011) lexical framework, we identified the presence of gender-coded language and assessed its relationship with salary and regional distribution. Our findings indicate that feminine-coded language was more prevalent in lower-paid roles and was significantly associated with reduced advertised salaries, even after adjusting for region. In contrast, masculine-coded language did not show a statistically significant effect. These patterns raise concerns about how seemingly subtle linguistic choices may reinforce pay disparities and hinder inclusive hiring in the data services workforce. This paper contributes to ongoing efforts to professionalise the field by offering insights to improve training, skill alignment, and recruitment equity.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: IASSIST
• Start Date: Jun 3, 2025
• End Date: Jun 6, 2025
• Deposit Date: May 29, 2025
• Peer Reviewed: Not Peer Reviewed
• Keywords: Data Services; gendered language in recruitment; recruitment in Data Services
• Public URL: https://uwe-repository.worktribe.com/output/14474334