As multicore processors are now the standard for high performance machines, scientists must develop complex programs to fully utilize the processing power. In this article we present NumCIL, a new framework for expressing scientific and financial algorithms. By using n-dimensional arrays, a scientist can write sequential code without knowledge of parallel constructs. The library is a CIL library and can thus be used from languages as diverse as IronPython, F# and C#. As the ndimensional arrays are compatible with numpy ndarrays, it is possible to run some existing numpy programs unmodified on NumCIL. The NumCIL library can run entirely in CIL or offload computation to an external library. We compare NumCIL to numpy and show that the implementation is able to perform on par with numpy for a variety of problems taken from imaging, physics and financial computing domains.