Trying to switch fields from PhD in computational biology to postdoc in machine learning/stats, how to increase likelihood of success? All of the following takes place in a UK university.

I have a BSc in Physics and an MSc in Computer Science. My thesis was on applying various machine learning/statistical techniques to biological datasets. I wanted to do something similar for my PhD, however my supervisor left the university. 

I am now in the first year of my PhD in Computer Science, specifically Computational Biology. My work focuses on comparing different techniques (physical/statistical/machine learning) in single cell simulations. I am finding it hard to incorporate machine learning techniques into my work as there aren't many datasets for the kind of thing my supervisor wants me to do and so the machine learning approach is proving tricky.

I desperately want a job/postdoc in a machine learning/stats environment. 


Lots of post docs I know switched field after their PhD e.g. Astro-physics to machine learning, dependable systems to machine learning, Biophysics to compiler design. In my case would anyone in the ML community take me seriously? (I thought my Msc would help me out...) 
I have taught myself a fair bit of ML and stats, is there anything else I should do to increase the likelihood of getting an ML/stats postdoc?
Would anyone in a stats department take me seriously as I have no maths degree?
Do people that change career areas have successful careers or is this normally a red flag?
My answer is based more on experience from computational biology, but I think it is relevant for other fields:


Changing fields is very common in academia, especially at the PhD/postdoc transition. In many cases it is actually considered an advantage, since you can import your skills, expertise and a certain thought-process into a field in which many people do not have those skills. For example, many physicists, computer scientists and mathematicians have migrated to biology and have made significant contributions. In fact, there are even postdoctoral fellowships that specifically fund this type of field-change.
Regarding your "will they take me seriously" questions: Since you are aiming mostly at applied ML/stats, I don't think you should be too concerned if the ML/stats theoretical community take you seriously. Many theorists tend to look down on applied science - don't worry about it, you can still have a significant impact without advancing any theory. It sounds like in the future you will either belong to the department in which you want to apply the techniques (e.g. a biology department) or will work very closely with people in those departments. In this case, you will usually be considered the ML/stats expert.
Having said all that, of course it is your job to become an expert. Teaching yourself the theory is important, but if you are going for applied science, especially applied ML/stats, it would be a big advantage to get actual experience in using them. There is a huge difference between learning about these methods and actually implementing and using them. You will see that during your PhD you can often expand your research in directions you are more interested in. It shouldn't be too difficult to use some ML/stats creatively in some sub-projects (which could later be expanded).