HN user
Those programming jobs exist because someone at some point discovered a problem that could be solved through statistical techniques. The difference is between moving quickly and exploring new problem spaces and really hammering home on well-defined problems that have solutions that need to be implemented in a way that looks a lot like normal software engineering.
Moving away from Null Hypothesis Testing and towards a more Bayesian approach is a good first step. For me, and I'm sure many others, NHT is a very backwards way of approaching inference. I don't care about an imaginary distribution with mean 0, I have real data I can fit to a distribution directly--what can you tell me about it? Conditioning on the data itself rather than an unobservable parameter of interest is much more intuitive and makes it much easier to report results to non-statisticians.