  While the open-source software development model has led to successful
large-scale collaborations in building software systems, data science projects
are frequently developed by individuals or small teams. We describe challenges
to scaling data science collaborations and present a conceptual framework and
ML programming model to address them. We instantiate these ideas in Ballet, a
lightweight framework for collaborative, open-source data science through a
focus on feature engineering, and an accompanying cloud-based development
environment. Using our framework, collaborators incrementally propose feature
definitions to a repository which are each subjected to an ML performance
evaluation and can be automatically merged into an executable feature
engineering pipeline. We leverage Ballet to conduct a case study analysis of an
income prediction problem with 27 collaborators, and discuss implications for
future designers of collaborative projects.
