The Chandra X-ray Observatory has been providing groundbreaking astronomical data since its launch by NASA in July of 1999. Now starting the second decade of science the Chandra operations team has been using Python to create predictive thermal models of key spacecraft components. These models are being used in the mission planning and command load review process to ensure that the series of planned observations and attitudes for each week will maintain a safe thermal environment. Speaking from my perspective as a scientist working to create and calibrate the models, I will discuss the process and the key off-the-shelf tools that made it all possible. This includes fitting many-parameter models with the Sherpa package, parallel computation with mpi4py/MPICH2, large table manipulations with pytables/HDF5, and of course fast array math with NumPy.