CS student here, interested in quant finance and currently building a small backtesting engine. I've noticed something about how I learn and wanted to know if this is normal in industry. I tend to focus much more on understanding the architecture, data flow, modelling decisions, and reasoning behind a system than memorizing library-specific Python syntax. For example, I can usually explain why a particular data structure, abstraction, or transformation exists, but I often need documentation or AI assistance for exact Pandas, NumPy, or library APIs. With modern tooling, is this considered a reasonable way to work professionally, or is strong API recall still expected for quant development/research roles? For those working in quant, trading systems, or research engineering: How much syntax do you actually remember? What separates a strong junior candidate from someone who just knows lots of Python?…