How do Data Science Workers Collaborate? Roles, Workflows, and Tools

TLDR

Data science has become a team activity in organizations, yet little is known about how these workers collaborate in practice. We surveyed 183 data science professionals about their interactions with colleagues and tools such as Jupyter Notebook. Teams collaborate across all workflow stages with diverse stakeholders and tools, and their documentation practices vary by tool type, suggesting design implications for supporting collaboration.

Abstract

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.