We propose Geomancy, a tool that reorganizes data to in- crease I/O throughput. Geomancy does not need any prior knowledge of the host system, instead it will build its own understanding of the system and determine the memory and computing capabilities of each node. In systems where heuristic-based improvements are inadequate or too resource intensive, Geomancy determines new placement policies by training a deep neural network with past workload and sys- tem traces. With CERN traces, Geomancy calculated an example placement policy for the scientific data accessed by workloads on the EOS. From system and workload data gathered on Pacific Northwest National Laboratory (PNNL) servers, we demonstrate a 49% increase in average through- put compared to the standard data layout for 50 runs of a physics simulation using a new placement policy calculated by Geomancy.