I feel like it is foremost a matter of attitude of the practitioner. An applied statistician and a machine learning engineer may deliver exactly the same end product, just the reasoning and assumptions differ. Machine learning uses little to no assumptions, where statisticians do. I also feel that machine learning engineers have a bit less fear of building black boxes.

Caruana showed the cartoon of the difference between a statistician and a machine learning practitioner by showing a cliff. The statistician carefully inches to the edge, stomping her feet to see if the ground is still stable, then 10 meters before the edge she stops and draws her conclusions. The machine learning practitioner dives headfirst from the cliff, with a parachute that reads "cross-validation".

See also:

http://norvig.com/chomsky.html On Chomsky and the Two Cultures of Statistical Learning.

And http://projecteuclid.org/euclid.ss/1009213726 Statistical Modeling: The Two Cultures by Leo Breiman.

and this joke:

> Norvig teamed up with a Stanford statistician to prove that statisticians, data scientists and mathematicians think the same way. They hypothesized that, if they all received the same dataset, worked on it, and came back together, they’d find they all independently used the same techniques. So, they got a very large dataset and shared it between them.

> Norvig used the whole dataset and built a complex predictive model. The statistician took a 1% sample of the dataset, discarded the rest, and showed that the data met certain assumptions.

> The mathematician, believe it or not, didn’t even look at the dataset. Rather, he proved the characteristics of various formulas that could (in theory) be applied to the data.