At the time of writing this, I have been working for Raytheon Missile Systems (RMS) for about 2.5 years since graduating college.
Prior to graduation, I was on track to go a more academic route due to graduate work and independent study projects I was involved in related to computational mathematics. I ended up choosing to try out industry first, however, since I had no experience with it and wanted to know if this path would be preferable for me long term.
While I have been successful at RMS and used my computational math background to achieve a lot there, I don't think industry is where I want to stay and so I am now working on applications so I can pursue a PhD and more academic career.
Given my industry background, are there any common beliefs in academia, whether good or bad, about individuals from industry I should be aware of for the sake of my application? Is there anything I should strive to highlight or avoid in my application that would help me fare better in the application process?
EDIT: My experience pre-RMS was largely in numerical analysis, optimization, and simulation, with emphasis in nonlinear systems of hyperbolic partial differential equations. One sample project, for example, was implementing a Space-Time Discontinuous Galerkin Finite Element code to tackle a system of Hyperbolic PDEs and doing validation and convergence studies to validate the theory. I also gained decent experience via independent studies and working at NASA JPL in computer vision. Other skills gained were in parallel computing using OpenMP and MPI.
At RMS, I have worked on clusters and have primarily developed and built codes in areas like numerical optimization, adaptive sampling of unknown functions, supervised machine learning, controls, bayesian estimation algorithms, and simulations.
In terms of research, I am planning to apply to computational math or computer science programs and hope to get involved in machine learning with emphasis in Reinforcement Learning for partially-observable Markov decision problems in continuous state and action spaces.