Employability skills: Profiling data scientists in the digital labour market. Issue 5 (October 2022)

• Record Type: Journal Article
• Authors: Smaldone, Francesco; Ippolito, Adelaide; Lagger, Jelena; Pellicano, Marco
• Is Part Of: European management journal. Volume 40:Issue 5(2022)
• Page Start: 671
• Page End: 684
• Publication Date: 2022-10
• Subjects: Data scientist -- Employability -- Labour market -- Skills -- Text mining -- Topic modelling; Management -- Periodicals; Management -- Europe -- Periodicals; Gestion -- Périodiques; Gestion -- Europe -- Périodiques; Management; Europe; Periodicals; Electronic journals; 658.0094
• DOI: 10.1016/j.emj.2022.05.005
• Languages: English
• ISSNs: 0263-2373

Abstract

Abstract: In the current scenario, data scientists are expected to make sense of vast stores of big data, which are becoming increasingly complex and heterogeneous in nature. In the context of today's rapid technological development and its application in a growing array of fields, this role is evolving simultaneously. The present study provides an insight into the current expectations of employers seeking to hire individuals with this job title. It is argued that gaining a better understanding of data scientists' employability criteria and the evolution of this professional role is crucial. The focus is placed on the desired prerequisites articulated through job advertisements, thus deriving relevant means for furthering theory and practice. It was achieved by harvesting relevant data from job advertisements published on US employment websites, which currently attract the US market's highest recruitment traffic. The key contribution of this study is to have identified means of systematically mapping skills, experience, and qualifications sought by employers for their data scientists, thus providing a data-driven pathway for employability and avoiding skills gaps and mismatches in a profession that is pivotal in the Industry 4.0.