Scrum is a popular methodology for PM in software engineering and recently the trend has carried over to data science. While the utility of Scrum in standard software engineering may remain up for debate, here I will detail why it has unquestionably no place in data science (and data engineering as well). This is not to say that “Agile” as a whole is bad for data science, but rather that the specific principles of Scrum: sprints, single product owner, scrum master, daily stand-ups (and the litany of other meetings) fit poorly for data science teams and ultimately result in poorer products.

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