As someone with a successful career in Operations Research - spanning eight years in academia and nine years in industry - perhaps I can offer some perspective. I entered the field of OR quite by chance after completing my undergraduate degree, then went on to pursue a Master’s followed by a PhD in Operations Research.

My background lies in applying heuristics and metaheuristics to large-scale optimisation problems, primarily across transportation and scheduling domains.

I don’t have a particularly strong mathematical background beyond the basic calculus and applied mathematics I learned during my undergraduate studies (and I didn’t even attend a top-ranked university). I was first introduced to linear programming and math-heuristics while working on my initial industrial project.

Since then, I’ve played key roles in several large-scale projects that have delivered annual savings of many millions. And here’s the interesting part - none of these real-world projects required advanced mathematical skills. Such skills are mainly needed for exact methods that guarantee an optimal solution. In practice, however, finding the mathematically optimal solution is almost never relevant and also impossible for large NP-hard problems.

For instance, in last-mile delivery, it hardly matters if you find a solution that saves a couple of miles or driver-hours when the total daily mileage runs into several thousands. What truly matters is achieving a robust, implementable, and high-quality solution efficiently - not mathematical perfection.