Contemplating about a second PhD in statistics/machine learning I have been thinking about a second PhD for the last year. I am currently a doctoral candidate in civil engineering at University of Texas at Austin (ranked 6th in my discipline) and working as a student statistical consultant at the university consulting center. I am working on spatio-temporal modeling of count data using Bayesian hierarchical models, with computationally efficient techniques for my dissertation. (I will have publications on this very soon; two of them are in review). I do have 2 publications in my area but their topic is the application of statistical models. I am also going to get a Masters in statistics next semester along with my PhD in civil engineering. I have done many courses related to Bayesian statistics including graduate level mathematical statistics, theoretical MCMC, stochastic volatility and time series models, statistical consulting, advanced econometrics (non-Bayesian), discrete choice modeling and one course on data mining (graduate level). In my field, I see massive datasets but very minimal statistical expertise, particularly on the big data side. This has motivated me to pursue something beyond my Phd and beyond my discipline. 

I am very interested in handling large datasets and perhaps, machine learning applications. I would like to know whether a PhD in machine learning is going to help me realise my dreams. I do not have any formal research experience in data mining or machine learning. But, I do a lot of Bayesian hierarchical modeling on smaller datasets. Given my experience, I am not sure whether I can secure admission to a good program in the machine learning area. I appreciate any suggestions and advice on whether to pursue another PhD and the feasibility of securing admission to a good program in a PhD machine learning track. I am assuming basic financial assistance for any PhD program. 

Thanks much in advance.
As other people already stated, multiple PhD's is almost certainly not a good idea. How to transition into machine learning depends on where you want to work:


academia, you can try to get a postdoc position in machine learning, in that way you can build your career by writing publications and get up to speed with machine learning during your postdoc research. This could be problematic as you might have to compete with people that have a PhD in machine learning. But this can be overcome during the interview stage. 
industry, you can simply apply to entry level machine learning positions (look for data science positions) and take it from there. Once you convince a company that you are a worthwhile addition, you can learn the ropes on the job.


In general, I would recommend already getting up-to-speed a bit in machine learning, try some online tutorials, etc. This can really help in securing a new position in either academia or industry.