We study the problem of detecting and locating change points in high-dimensional models with low rank structure. We develop a simple two step algorithm for the problem at hand and establish performance guarantees in the form of finite sample bounds for the accuracy of the estimated locations of the change points and the underlying model parameters. We illustrate the detection strategy on data from three different domains that employ different statistical models: macroeconomics, neuroimaging and political science.