Drawing on theories from the sociology of work and the sociology of culture, this article argues that members of nascent technical occupations construct their professional identity and claim status through an omnivorous approach to skills acquisition. Based on a discursive analysis of 56 semi-structured in-depth interviews with data scientists, data science professors and managers in Israel, it was found that data scientists mobilise the following five resources to construct their identity: (1) ability to bridge the gap between scientist’s and engineer’s identities; (2) multiplicity of theories; (3) intensive self-learning; (4) bridging technical and social skills; and (5) acquiring domain knowledge easily. These resources diverge from former generalist-specialist identity tensions described in the literature as they attribute a higher status to the generalist-omnivore and a lower one to the specialist-snob.

In this comment to Noordegraaf’s ‘Protective or connective professionalism? How connected professionals can (still) act as autonomous and authoritative experts’, we argue that Noordegraaf has contributed significant insights into the development of contemporary professionalism. However, we argue for a less binary and more complex view of forms of professionalism, and for finding ways of understanding professionalism grounded in a relational view of everyday professional work. The first section (by Johan Alvehus) suggests that Noordegraaf’s ‘connective professionalism’ is primarily about new ways of strengthening professionalism’s protective shields by maintaining functional ambiguity and transparent opacity around professional jurisdictions. The second section (by Amalya Oliver and Netta Avnoon) argues for viewing professionalism on a range of protection–connection and offers an approach for understanding how connective and protective models co-occur. Both commentaries thus take a relational, dynamic, and somewhat skeptical view on the reproduction and maintenance of professionalism.

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Research on AI ethics tends to examine the subject through philosophical, legal, or technical perspectives, largely neglecting the sociocultural one. This literature also predominantly focuses on Europe and the United States. Addressing these gaps, this article explores how data scientists justify and explain the ethics of their algorithmic work. Based on a pragmatist social analysis, and of 60 semi-structured interviews with Israeli data scientists, we ask: how do data scientists understand, interpret, and depict algorithmic ethics? And what ideologies, discourses, and worldviews shape algorithmic ethics? Our findings point to three dominant moral logics: (1) ethics as a personal endeavor; (2) ethics as hindering progress; and (3) ethics as a commodity. We show that while data science is a nascent profession, these moral logics originate from the techno-libertarian culture of its parent profession—engineering. Finally, we discuss the potential of these moral logics to mature into a more formal, agreed-upon moral regime.

How do data scientists frame their relations with domain experts? This study focuses on data scientists’ aspired professional jurisdiction and their multiple narratives regarding data science’s relations to other fields of expertise. Based on the analysis of 60 open-ended, in-depth interviews with data scientists, data science professors, and managers in Israel, the findings show that data scientists institutionalize three narratives regarding their relations with domain experts: (a) replace experts, (b) absorb experts’ knowledge, and (c) provide a service to experts. These three narratives construct data scientists’ expertise as universal and omnivorous; namely, they are relevant to many domains and allow data scientists to be flexible in their claim for authority.