Then you go off on a jag about how simple the math is and how easy to implement the algorithms are. Totally true, but this isn't what data scientists are paid for. Data science/machine learning positions are more about understanding the limitations and pathologies of the algorithms, the data, and their interactions. This isn't necessarily hard, but it can be -- and the pay tends to scale in proportion to the difficulty. Since theory doesn't provide much guidance -- you can learn everything that anyone knows in a year -- employers will necessarily prefer someone who can demonstrate practical experience with data. Selecting for PhDs is one way of filtering for that.
If you've got experience with data, emphasize that and you should get callbacks.