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Data scientists/analysts, how do you prioritize work, esp. if you work with multiple teams?
Some issues I’ve seen are needing to do work to estimate work needed, maintaining trust if say no a lot, and finding time for work with long-term benefits (e.g. making a pkg).
Some issues I’ve seen are needing to do work to estimate work needed, maintaining trust if say no a lot, and finding time for work with long-term benefits (e.g. making a pkg).
I'm a fan of @skyetetra's article https://towardsdatascience.com/prioritizing-data-science-work-936b3765fd45?gi=2cf273183a6a, but I'm interested in hearing other ideas and also how to put it into practice (e.g. after you decide which projects to prioritize, how do you talk to the teams and maintain a good relationship)?
Some related questions:
- How do you deal with ad-hoc requests?
- For working with multiple teams, does each team always get X hours per week, or does it depend?
- Do you invest in helping people answer their own questions (e.g. through training or developing a dashboard)?
- How do you deal with ad-hoc requests?
- For working with multiple teams, does each team always get X hours per week, or does it depend?
- Do you invest in helping people answer their own questions (e.g. through training or developing a dashboard)?
