How do Data Science Workers Collaborate? Roles, Workflows, and Tools
This work addresses the lack of understanding of collaboration in data science teams, providing insights for designing better tools and workflows, though it is incremental as it builds on existing knowledge of team dynamics.
The study investigated how data science teams collaborate in practice by surveying 183 participants, finding that teams are highly collaborative across six workflow steps and that practices like documentation vary with tool usage.
Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model). We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.