Paper Short Abstract

The interdisciplinary nature of the data science workforce extends beyond the notion of traditional data scientists. Successful teams require a range of technical expertise, domain knowledge and leadership skills. To reinforce a team-based approach, we recommend diversifying data science roles.

Paper Abstract

Data science is an interdisciplinary and collaborative field. The specific skills required for any data science initiative depend on the discipline, industry and the context within which data-informed solutions are developed. Effective data science teams should therefore be composed of diverse specialists who can flexibly combine their expertise to develop innovative solutions that address both societal and industry needs.

To build and strengthen the data science workforce, institutions, funders, and policymakers must invest in developing and diversifying these specialised professional roles, fostering a resilient data science ecosystem for the future. By recognising the diverse specialist roles that contribute to interdisciplinary teams, organisations can leverage deep expertise across a broad range of skills. This approach enhances responsible decision-making and fosters innovation at every level.

Building on existing standards and professional personas for AI and Data Scientists, and drawing upon examples of diverse professionals from The Alan Turing Institute (the UK's national institute for data science and AI), this paper/talk will share evidence-based recommendations to support the professionalization of different specialist roles. We further provide a detailed Skills and Competency Framework for Research Community Manager roles, offering a template for articulating skills and competencies for other newly emerging roles.

Our overarching aim is to shift the perception of data science professionals away from the traditional view of the lone "data scientist" and toward a competency-based model of multiple specialized roles. Through collaboration within a data science project team, members complement and combine their skills—each crucial to the success of data science initiatives.