How can I transition from academia to become a data scientist in the industry?

[StatGuy2000]

At least for supervised learning, I think the only differences from traditional statistics are that the number of parameters is larger, and optimization is nonconvex.

From what I understand, there has been much fertile research within statistics on working in domains of large number of parameters, dimensionality reduction, and sparse signal detection (e.g. recent research on higher criticism -- see the following link: https://arxiv.org/pdf/1411.1437.pdf)

Researchers working in machine learning also work on many of these same problems, so increasingly there is a blurring of disciplines between the machine learning and statistics communities. As a matter of fact, it is not uncommon for researchers to be cross-listed between the statistics and computer science departments (where such departments exist separately). My alma mater, for example, have 3 faculty members cross-listed as such.