For pure SWE I don't think I have a great answer for you, but on the ML/DE side there are a lot of industry-specific problems, especially on the P&C side, it he

For pure SWE I don't think I have a great answer for you, but on the ML/DE side there are a lot of industry-specific problems, especially on the P&C side, it helps to have context and experience with: * Rating (a deep, classical, tabular ML problem) is still catching up to SOTA techniques in many insurance companies + uneven regulatory environment makes it tricky to navigate. * Marketing is another classical ML problem, the goal is to optimize ad/lead broker spend/conversion funnel. * CS of course has huge opportunity with LLMs. * Automated claim fraud detection is a very difficult problem amenable to LLMs/MM DL models. Lots of "pivot to AI" going on here. And there are others of course. The main thing is that insurance companies have limits on product innovation - policy terms are in many cases mandated by law, so the main differentiator is operational efficiency and exellency. Solutions that move the needle on big revenue/cost levers are very valuable and can be applied widely.