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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& #39;m a fan of @skyetetra& #39;s article https://towardsdatascience.com/prioritizing-data-science-work-936b3765fd45?gi=2cf273183a6a,">https://towardsdatascience.com/prioritiz... but I& #39;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)?
