This paper makes two important contributions to understand the mobility patters of scientists. First, by combining two large-scale data sets covering the publications of 3.5 mio scientists over 60 years, we are able to reveal the geographical ‘career paths’ of scientists. Each path contains, on the individual level, information about the cities (resolved on real geographical space) and the time (in years) spent there. A statistical analysis gives empirical insights into the geographical distance scientists move for a new affiliation and their age when moving. From the individual career paths, we further reconstruct the world network of movements of scientists, where the nodes represent cities and the links in- and out-flow of scientists between cities. We analyze the topological properties of this network with respect to degree distribution, local clustering coefficients, path lengths and assortativity. The second important contribution is an agent-based model that allows to reproduce the empirical findings, both on the level of scientists and of the network. The model considers that agents have a fitness and consider potential new locations if they allow to increase this fitness. Locations on the other hand rank agents against their fitness and consider them only if they still have a capacity for them. This leads to a matching problem which is solved algorithmically. Using empirical data to calibrate our model and to determine its initial conditions, we are able to validate the model against the measured distributions. This allows to interpret the model assumptions as microbased decision rules that explain the observed mobility patterns of scientists. Reproducing scientists’ mobility: A data-driven model