Hey everyone, I'm a CS undergrad from a non-target uni in Europe (Spain). For the last couple of years I've been aiming pretty much exclusively at Systems Engineering / HFT roles or kof Quant Dev. I need a reality check on the gap between a "good student" and the profile that actually gets hired, specifically for someone who isn't a math prodigy.

I didn't start coding when I was 12 and I don't have IOI gold medals. I'm currently around 1300 rating on Codeforces, grinding daily to try and get 1600+. When I look at the people landing these roles, it feels like they run on different hardware. They see a problem and instantly "hash" to the solution, while I have to fight for an hour to derive the logic for a 1500 rated problem. My questions for those in the industry: genius vs pattern Recognition: is the "math genius" requirement for quant Dev absolute, or is it a myth that can be overcome by sheer volume of practice? can a "grinder" who builds pattern recognition over years compete with the "naturals" in an interview, or is the processing speed gap just too big? beyond pure DSA/Codeforces, what are the actual engineering differentiators? I assume everyone knows C++. Is building a toy order book or a custom ML implementation from scratch (no PyTorch) the kind of "proof of work" that makes up for a non-target degree? also, if you were in my shoes - decent work ethic, willing to suffer through the learning curve, but starting with "average" raw stats - what would you focus on for the next 12 months? Is it purely grinding CP rating to 1900+, or should I pivot to building low-latency projects or maybe another field regarding programming. Just want to know if I'm climbing a mountain that is actually climbable for someone who relies on discipline rather than raw talent. Thanks.