Unicorn data scientist: the rarest of breeds. Issue 1 (3rd April 2017)

• Record Type: Journal Article
• Authors: Baškarada, Saša; Koronios, Andy
• Is Part Of: Program. Volume 51:Issue 1(2017)
• Page Start: 65
• Page End: 74
• Publication Date: 2017-04-03
• Subjects: Data analytics -- Skills -- Definition -- Framework -- Data science -- Business analytics; Libraries, University and college -- Great Britain -- Automation -- Periodicals; 025.30285
• DOI: 10.1108/PROG-07-2016-0053
• Languages: English
• ISSNs: 0033-0337

Abstract

Abstract : Purpose: Many organizations are seeking unicorn data scientists, that rarest of breeds that can do it all. They are said to be experts in many traditionally distinct disciplines, including mathematics, statistics, computer science, artificial intelligence, and more. The purpose of this paper is to describe authors' pursuit of these elusive mythical creatures. Design/methodology/approach: Qualitative data were collected through semi-structured interviews with managers/directors from nine Australian state and federal government agencies with relatively mature data science functions. Findings: Although the authors failed to find evidence of unicorn data scientists, they are pleased to report on six key roles that are considered to be required for an effective data science team. Primary and secondary skills for each of the roles are identified and the resulting framework is then used to illustratively evaluate three data science Master-level degrees offered by Australian universities. Research limitations/implications: Given that the findings presented in this paper have been based on a study with large government agencies with relatively mature data science functions, they may not be directly transferable to less mature, smaller, and less well-resourced agencies and firms. Originality/value: The skills framework provides a theoretical contribution that may be applied in practice to evaluate and improve the composition of data science teams and related training programs.