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Abstract GitLab is a software company that works “all remote” at the scale of more than 1000 employees located in more than 60 countries. GitLab has no physical office and its employees can work from anywhere they choose. Any step of the organizational life of a GitLab employee (e.g., hiring, onboarding and firing) is performed remotely, except for a yearly companywide gathering. GitLab strongly relies on asynchronous coordination, allowing employees to work anytime they want. After highlighting some of the main practices implemented by GitLab to effectively work all remotely and asynchronously, I asked renowned organizational scientists their thoughts on this interesting case and to question the generalizability of the all remote asynchronous model. Understanding whether and under what conditions this model can succeed can be of guidance for organizational designers that are now considering different remote models in response of the COVID-19 shock and its aftermath. 

>With the increasing ability to generate actionable insight from data, the field of data science has seen significant growth. As more teams develop data science solutions, the analytical code they develop will need to be enhanced in the future, by an existing or a new team member. Thus, the importance of being able to easily maintain and enhance the code required for an analysis will increase. However, to date, there has been minimal research on the maintainability of an analysis done by a data science team. To help address this gap, data science maintainability was explored by (1) creating a data science maintainability model, (2) creating a new tool, called MIDST (Modular Interactive Data Science Tool), that aims to improve data science maintainability, and then (3) conducting a mixed method experiment to evaluate MIDST. The new tool aims to improve the ability of a team member to update and rerun an existing data science analysis by providing a visual data flow view of the analysis within an integrated code and computational environment. Via an analysis of the quantitative and qualitative survey results, the experiment found that MIDST does help improve the maintainability of an analysis. Thus, this research demonstrates the importance of enhanced tools to help improve the maintainability of data science projects.