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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I perceiving caring about accuracy first as ML eng territory, and I perceive MLE as stemming directly from computer science. This is sort of silly of me, given that "data scientist" has been an overloaded junk title for most of the time I've held it (and if I'm honest still is)
Specialization within the DS world is still emerging and while some of the boundaries between types of DS roles have gotten sharper in the last few years, they're all still coming from the same lineage
"Terrabytes of data are pouring into computers from many sources, both scientific, and commercial, and there is a need to analyze and understand the data," Breiman says, like a prophet foretelling HBR articles to come
And reflecting on his work in the 90's (!!!), he was already seeing how crossfunctional this line of work can be: "there has been a noticeable move toward statistical work on real world problems and reaching out by statisticians toward collaborative work with other disciplines"
The problems we're solving with data today are greater in scale and complexity, which means we're getting the luxury of focusing on narrower subsets of those problems. Now we ask for analysts, scientists, MLEs, analytics engineers, etc. instead of just DSes or statisticians
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