Computational science has made rapid progress in recent years, leading to ever increasing demand for supercomputing resources. For scientific applications that leverage such resources, Message Passing Interface (MPI) plays a crucial role in enabling distributed memory parallelization across multiple nodes. However, parallelizing MPI code manually, and specifically, performing domain decomposition, is a challenging and error-prone task. In this paper, we address this problem by developing MPI-rical, a novel data-driven, programming-assistance tool that assists programmers in writing domain decomposition based distributed memory parallelization code using MPI. Specifically, we leverage Transformer architecture - the invention that led to advancements in the field of natural language processing (NLP) - with a supervised language model to suggest MPI functions and their proper locations in the code on the fly. In addition to the novel model for MPI-based parallel programming, in this paper, we also introduce MPICodeCorpus, the first publicly-available corpus of MPI-based parallel programs that is created by mining more than 15,000 open-source repositories on GitHub. Experimental results demonstrate the effectiveness of MPI-rical on both dataset from MPICodeCorpus and more importantly, on a compiled benchmark of MPI-based parallel programs for numerical computations that represent real-world scientific applications. Specifically, MPI-rical achieves F1 scores between 0.87-0.91 on these programs, demonstrating its accuracy in suggesting correct MPI functions at appropriate code locations. The source code used in this work, as well as other relevant sources, are available at: https://github.com/Scientific-Computing-Lab-NRCN/MPI-rical.