Retention is indeed a problem. I don’t think I qualify as a “woman in AI,” but I am a cis woman who has trained (well, fine-tuned) my own models on my gaming PC at home as a hobby. Several years ago, I fucked off and became a professional photographer after working for a Fortune 50 for a decade; I loved my job but hated the sexism. There’s almost no amount of money that could get me back to working in tech.
(Incidentally, a bunch of my images were scraped and used in the training data set for Stable Diffusion. I’m mad about this and have no desire to help corporations profit off others’ art.)
From my experience ML and data science in general are very welcoming to women and people with very different backgrounds. Also the way of working is very different. Agile doesn’t really work, because is a non-deterministic world, you have relatively “long” projects, no PM chasing burndown (burnout) story points (or whatever those silly metrics are called), curious and interesting colleagues that are there for passion. You can give it a try. As said, in many industries, women in data science and ML are highly valued and unfortunately there are not enough of them.
Retention is indeed a problem. I don’t think I qualify as a “woman in AI,” but I am a cis woman who has trained (well, fine-tuned) my own models on my gaming PC at home as a hobby. Several years ago, I fucked off and became a professional photographer after working for a Fortune 50 for a decade; I loved my job but hated the sexism. There’s almost no amount of money that could get me back to working in tech.
(Incidentally, a bunch of my images were scraped and used in the training data set for Stable Diffusion. I’m mad about this and have no desire to help corporations profit off others’ art.)
From my experience ML and data science in general are very welcoming to women and people with very different backgrounds. Also the way of working is very different. Agile doesn’t really work, because is a non-deterministic world, you have relatively “long” projects, no PM chasing burndown (burnout) story points (or whatever those silly metrics are called), curious and interesting colleagues that are there for passion. You can give it a try. As said, in many industries, women in data science and ML are highly valued and unfortunately there are not enough of them.