My god, you are the one who wrote this and you don't understand why your sample is so biased? No wonder it is so horrible.
How many teachers are in your sample? How many nurses? (extremely few - how would the tool capture that?)
Or more insiduously compare the number of engineers to the number of people in marketing. Both are "knowledge workers", but they have a very different gender breakdown. And they also have a very different day. Someone in engineering is on their computer all day, someone in marketing or sales probably not. If you are making sales calls all day instead of looking at an IDE, you can be vastly more productive than an engineer staring at a blank screen, but that doesn't show up in RescueTime at all. It's one thing one you are on a team and can account for that, it is another when you make a generalization about the population.
All of your data can be explained with other rational besides "men are more productive than women". Your company approaches the world from the view of engineers to begin with, and then slaps that bias on top of a faulty set of data. You should be careful with this or you are going to have NOW breathing down your neck (I guess all press is good press, but do you really want to make your product piss of women to that extent?).