We explore how recommendation techniques can be adapted and applied to big data science. Using Globus we derive features specific to big data science and develop a set of data location prediction heuristics. We combine these heuristics into a single recommendation engine using a deep recurrent neural network. We show, via analysis of historical Globus data, that our approaches can predict the storage locations used in user-submitted data transfers with 78.2% and 95.5% accuracy for top-1 and top-3 recommendations, respectively. We presented this work as a SRC poster and an extension as a workshop paper at Supercomputing ‘16. The SRC poster won Best Undergraduate Poster! My amazing mentor Kyle Chard also won the Early Career Researchers in High Performance Computing Award!