We noticed some common patterns in our engagements with scientific users that use Jupyter for their computational workflows on NERSC systems. At the highest level there is a need for combining exploration of very large datasets with some computational and analytical capabilities. Crucially the scale of data or compute (or both) required to enable these workflows typically exceeds the capacity of the users own machines and the users need a user-friendly way to drive these large-scale workflows interactively.