How Do Data Science Workers Collaborate?

Data Science Roles, Workflows, and Tools

As more and more organizations adopt advanced data-driven approaches for improved decision-making, more and more data science teams are increasingly working on massive data sets, pipelines, and more consequential decisions and products. But with data science workflows comprising multiple phases, comes the question, “Do data science workers collaborate?” If yes, how exactly?

In this paper, researchers with IBM and MIT present results of a large-scale survey of data science workers at a significant corporation that examined how data science workers collaborate.

Survey Participants & Questions

Researchers designed an online survey and recruited 183 participants who have experience working in data science teams. Survey questions cover five critical roles in a data science team (engineer/analyst/programmer, researcher/scientist, domain expert, manager/executive, and communicator).

They include six stages in a data science workflow. Participants were also asked to describe their collaborative practices around code and data sharing and re-use, including their expectations around their work and their documentation practices.