Dear Operations Research community,
I am seeking some career advice regarding my current position as an Operations Research Analyst. I have been working in this role for three years, but I lack a formal academic background in Operations Research and Numerical Optimization. I got this job by demonstrating other related skills such as numerical computation, evolutionary optimization, computer programming, statistics and basic ML/Deep Learning.
Although I am confident in my basic knowledge of Operations Research and can implement optimization problems in AML's (Pyomo mainly) using solvers, I often feel that my lack of theoretical understanding limits my ability to go deeper into the concepts. I struggle when discussing topics like algorithmic implementation details of solvers, branch and bound, optimality, KKT conditions, or gap in MIP's, big-M method, among others. I hope that provides an idea regarding where I struggle.
As I want to remain in the field of OR, I am willing to put in efforts to improve my knowledge. However, I struggle to find relevant resources that can help me fill the gaps in my OR knowledge. While there are plenty of resources available for topics in ML/Data Science, I cannot find many resources in OR that can help me improve.
Therefore, I would like to ask for your advice on how I can develop my OR knowledge. Should I consider reading a book, taking formal training, or is there any other resource that you can recommend? Any advice will be highly appreciated.
Thank you in advance.