  Data science workers increasingly collaborate on large-scale projects before
communicating insights to a broader audience in the form of visualization.
While prior work has modeled how data science teams, oftentimes with distinct
roles and work processes, communicate knowledge to outside stakeholders, we
have little knowledge of how data science workers communicate intermediately
before delivering the final products. In this work, we contribute a nuanced
description of the intermediate communication process within data science
teams. By analyzing interview data with 8 self-identified data science workers,
we characterized the data science intermediate communication process with four
factors, including the types of audience, communication goals, shared
artifacts, and mode of communication. We also identified overarching challenges
in the current communication process. We also discussed design implications
that might inform better tools that facilitate intermediate communication
within data science teams.
