Why Data Science Teams Need Generalists, Not Specialists

An end-to-end algorithmic business capability requires many data functions and companies usually create teams of specialists: research scientist, data engineers, machine learning engineers, causal inference scientists, and so on. Specialists’ work is coordinated by a product manager, with hand-offs between the functions in a manner resembling the pin factory: “one person sources the data, another models it, a third implements it, a fourth measures it” and on and on. These costs scale super-linearly with the number of people involved.2 When data scientists are organized by function the many specialists needed at each step, and with each change, and each handoff, and so forth, make coordination costs high.

Source: multithreaded.stitchfix.com