Computer Scientist Analyst

This brings computational thinking to event analysis: complexity theory, algorithm design, systems architecture, and computability limits. You'd reach for it when evaluating technical feasibility, analyzing scalability constraints, or spotting fundamental computational barriers in proposed systems. It covers the classics like Big O analysis, P vs NP implications, the halting problem, and practical trade-offs between time, space, and correctness. The approach is rigorous, grounded in formal CS theory rather than hand-waving. Honest take: most useful when you need to distinguish between "this is slow" and "this is mathematically intractable," or when someone's proposing something that bumps into Rice's theorem without realizing it.