  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.
