Actually the late Prof Leo Breiman expanded this metaphor into the statistical modelling world. The two cultures being the explanatory and predictive modelling folk.
The explanatory modelling culture (that he calls the data modelling culture) being those that first come up with a guess of how the data is generated and then try to test that hypothesis using goodness of fit measures, and the predictive modelling culture (he calls algorithmic modelling culture) being the modern machine learning researchers that are purely interested in predictive power.
I have a masters in HEP and have experience working on the CMS experiment at CERN for a year as part of my Masters.
I left physics for data privacy in 2017 (PhD programme).
The main reason was that I didn't really feel like I was tackling real issues (real and immediate to society) if I continued work in physics.
The swap has been difficult and I am at a disadvantage wrt to colleagues that come from more relevant backgrounds (e.g. Computer science or Applied maths) but at the end of the day I feel a lot better about the contribution of the work (to society) and also about job security in the future.