What are the potential pitfalls of having a PhD in machine learning? This is a follow up question from this one. From what I read in that question I understood that in most cases the PhD is not a good idea to grow a good career in the industry. 

However I contacted two people in the industry who have PhDs in machine learning and data mining and they work in Amazon Germany as machine learning scientists or data miners. They told me almost any job in data mining or machine learning requires a PhD.

My question is: do the same pitfalls for other fields also apply for PhDs in machine learning and data mining? If not, then are there still other pitfalls?
As a person who has hired quite a few people in industry, I will simply say that advanced areas (or even areas that are perceived to be advanced) often see PhDs being preferable. In this respect I completely agree with Bitwise.

I do not, however, agree that you might earn less. Those with sought-after skills get paid well in industry. It is about supply and demand but supply (people who can do the job) is not high and demand is growing.

Machine-learning is hot in the business literature. That is, managers are becoming well informed about the potentials of machine-learning and they are becoming worried that the only way they can compete for the long-term is by taking advantage of this area.

While lower-level programming jobs might not see any benefit from higher qualifications, areas like machine-learning are not seen as lower-level.

To answer your main question, are there pitfalls of having a PhD if you want a job in industry? The answer is no. I am unaware of anyone who has ever thought lower of a job candidate because they have a PhD. Yes, you could get a job without one but for your areas of interest, a PhD will give you lots of advantage when it comes to getting job offers.