I made a similar move to industry work many years ago. My advice is to clarify what interests you, as data science and machine learning are huge fields. Also "machine learning" is still hard to pin down what that even means these days. If you're willing to start off in a data science role without a heavy machine learning aspect to it, that's very easy; there's tons of companies (startups like Uber and more mature companies like Facebook) that have entire nap rooms of data scientists with PhD's working there and some super interesting data to work with.
If there is a specific area of data science and/or machine learning or other work going on that interests you that you come across, email a PhD at the company doing this work. Many of these people still publish papers at conferences or at the very least have blogs describing what they are working on. Find a company that's doing work that interests you and email the researcher. Don't go through HR. Email the person directly and ask to chat. I found PhD's working in industry to be very receptive to talk to someone qualified and with an interest in the work as they have likely been in your shoes before. Go from there. GL!
The program is specifically for PhDs who are in your position, aka moving from academia to industry. I believe the course is available in Boston, New York and remotely (not 100% sure of the latter).
I'm in a similar boat. I studied Economics in undergrad, but I only have a Masters (in an unrelated field) so I didn't meet the critieria.
The problem you may face, is you will need to know how to program in a 'production' environment. So were as in academia you can be a single person running code, in industry, you will have multiple people utilise and check your code. It will have to be robust and scalable. So you may need to brush up on programming skills, though you could be great from the start.
Something I've seen with Academics, is that whilst they are great at solving detailed problems, they sometimes fail to present the impact of their work in a business context, or what they can offer and how it solves business problems, rather than just "interesting" problems.
My advice would be to apply and see what comes back, and get feedback from the people you apply to.
The reason I am asking this is because I'm enrolled in an econ PhD myself (spec. in advanced econometrics) and I am extremely interested in machine learning and data science. I am constantly looking for ways to combine the two in interesting ways. It's nice to meet someone like-minded.