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.

中文翻译：

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数据科学工作者如何协作？角色、工作流程和工具

今天，数据科学在组织内的重要性已经催生了数据科学工作者团队合作从数据中提取洞察力，而不是单独工作的数据科学家。然而，我们仍然缺乏对数据科学工作者在实践中如何协作的深刻理解。在这项工作中，我们对 183 名从事数据​​科学各个方面工作的参与者进行了在线调查。我们专注于他们报告的彼此之间的交互（例如，经理与工程师）和不同工具（例如，Jupyter Notebook）。我们发现，数据科学团队在数据科学工作流程的六个常见步骤（例如，清理数据和训练模型）中非常协作并与各种利益相关者和工具合作。我们还发现员工采用的协作实践，例如文档、根据他们使用的工具种类而有所不同。基于这些发现，我们讨论了支持数据科学团队合作和未来研究方向的设计含义。