How do you prioritize data science projects when you have more requests than capacity? What frameworks or criteria do you use for making these decisions?

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Prioritization is essential because data science teams are almost always capacity-constrained with more potential projects than they can pursue. Effective prioritization ensures the team works on what matters most rather than simply responding to whoever asks loudest.