Wrong but useful. Working @Twitter, tweeting about data science and people management.
"Wait, it's all her views?" 
"Always has been"
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Which brings us back to communication. Making sure all the DS on your team have the same baseline mental models goes a long way towards enabling effective collaboration across seemingly unrelated work streams.
Anyway, developing a shared mental model of your company's problem space IS a serious part of the Data Science profession. I don't know if it's accurate to say knowledge sharing is to data science as peer review is to software engineering, but I feel like there's something there.
maybe knowledge sharing is to data science as abstraction sharing is to software engineering? it feels like often times the culprit behind software systems behaving poorly is that we represent the same concept / process in slightly different and incompatible ways across systems
That's a really great point. It makes me think about how much grief is caused by different understandings of what a "user" is, especially when that gets baked into different metrics and goals
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