Because of how gaussian distributions work, the differences at the outlying values are huge even for small differences in the average.
For example, men are taller than women in general, but of course some men are taller than some women.
But at the extreme heights, the ratio between men and women becomes stagger. At 6 feet tall, men outnumber women 30:1. At 6'3", 2000:1.
Now apply this to tech. If we imagine a relatively elite job, like being a professional programmer with significant responsibility, that is the kind of thing only someone at the extreme end of the bell curve of tech-proclivity is going to do. But because of the way gaussians work at extreme values, even if women on average have similar tech-proclivity to men, at that high level the ratio of men:women could be huge.
All this could just be due to a simple misunderstanding of the math of a bell curve distribution and how it works at the ends of the spectrum.