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Why Data Science Teams Need Generalists, Not Specialists

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Mar 12, 2019 10:10 AM 1+ mon ago

Why Data Science Teams Need Generalists, Not Specialists

In The Wealth of Nations, Adam Smith demonstrates how the division of labor is the chief source of productivity gains using the vivid example of a pin factory assembly line: "One [person] draws out the wire, another straights it, a third cuts it, a fourth points it, a fifth grinds it." With specialization oriented around function, each worker becomes highly skilled in a narrow task leading to process efficiencies. Output per worker increases many fold; the factory becomes extremely efficient at producing pins.

This division of labor by function is so ingrained in us even today that we are quick to organize our teams accordingly. Data science is no exception. An end-to-end algorithmic business capability requires many functions, and so companies usually create teams of specialists: research scientist, data engineers, machine learning engineers, causal inference scientists, and so on. Specialists' ...

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