I think Statistics departments have lagged behind in teaching ML techniques as first class citizens of the statistical world, but also that ML folks tend to gloss over or ignore a lot of the benefits formal inference and statistical thinking bring to the table.
Traditional statistics is very very good at helping us learn a lot about relatively simple (or carefully and deliberately simplified) processes, and provides a rich background in study design.
ML techniques are good at helping us learn a little bit about arbitrarily complicated processes, and apply that knowledge quickly. A modern practitioner in either field should have a working knowledge of both [families of] paradigms.
Traditional statistics is very very good at helping us learn a lot about relatively simple (or carefully and deliberately simplified) processes, and provides a rich background in study design.
ML techniques are good at helping us learn a little bit about arbitrarily complicated processes, and apply that knowledge quickly. A modern practitioner in either field should have a working knowledge of both [families of] paradigms.