Van Allen radiation belt electron dynamics are governed by a multitude of physical processes that can simultaneously drive acceleration, transport and loss. However, each individual process can be linked to a specific energy‐dependent pitch angle distribution (PAD). We employ a new, unsupervised machine learning technique on 7‐year of Van Allen Probe Relativistic Electron‐Proton Telescope data and discover that six PADs successfully describe 93% of outer belt relativistic electrons, two each of: pancake, butterfly, and flattop. We investigate the occurrence and storm‐time evolution of each PAD through 45 geomagnetic storms. We find new populations of PADs, including: “shadowing‐like” and wave‐particle interaction signatures at low‐L, and radial diffusion and substorm injections at higher‐L, as well as determining that wave‐particle interaction dominated PADs are swamped by radial diffusion processes through geomagnetic storms. Our results clearly demonstrate that PAD characterization is a key component of understanding Van Allen radiation belt electron dynamics.