Description: Data science and artificial intelligence are becoming ever more prevalent in the UK with common needs and challenges arising. Solving these challenges requires novel tools, practices and systems which can unlock advances across the wider sector and accelerate innovation. The Alan Turing Institute’s Tools, Practices and Systems (TPS) programme represents a cross-cutting set of initiatives which seek to build open source infrastructure that is accessible to all and empower a global, decentralised network of people who connect data with domain experts. The core missions of the programme are to build trustworthy systems, embed transparent reporting practices, promote inclusive interoperable design, maintain ethical integrity, encourage respectful co-creation and champion open leadership roles. One of the biggest investments that the Turing institute has made to ensure that TPS can reach its goals is by helping establish teams with experts representing various research infrastructure roles. Building on the success of Research Engineering Groups, the TPS teams bring expertise in community management, and research application management (product management equivalent) while closely collaborating with data stewards, data wranglers, citizen scientists, ethnographers and ethicists to operationalise TPS’ mission in data science and AI projects at the Turing and beyond. Underlying these infrastructure roles are open, reproducible and ethical research practices guided through the TPS’ flagship project, The Turing Way - an open source community-led guide to collaborative data science. The Turing Way's goal is to provide all the information that researchers, industry professionals, government and members of the public need to understand reproducibility and ethical standards in data science at all stages of development. In this talk, I will present my work at the Turing Institute (Tools, Practices and Systems programme; and The Turing Way project) and with The Turing Way that engages people, which have designed, implemented and strengthened research and policies for supporting both traditional and emergent research infrastructure roles such as community managers, research engineers, open source experts, data stewards, ethicist and other non-traditional positions at organisation levels. Drawing from some successful examples, I will discuss where investments in research infrastructure roles are crucial to influencing meaningful culture change in modern data science and AI. This talk partly builds on a previous workshop delivered at AI-UK 2022: https://www.youtube.com/watch?v=ZukCcGfa_Rg