One problem with hiring academics is that they can be far too focused on their subdiscipline as this is necessary to break new ground. An astro physicist might

One problem with hiring academics is that they can be far too focused on their subdiscipline as this is necessary to break new ground. An astro physicist might be amazing at identifying peaks in tremendous amounts of data but have no idea how to do basic analysis of a graph. When we interview someone I like to start talking/asking about matrix decomposition (eigenvectors, svd ect) and see how excited they get. I consider knowing about things like MDS and pagerank a bare minimum and if someone can bring up a more recent or esoteric application (locally linear embedding, graph partitioning) they stand out. Asking about the nuances of estimating probability densities from data (bin/histogram vs kde etc) is another good one and something that stops a lot of the cooler theoretical statistics and information theory from getting used (or used well) in the real world. Both of these questions get more at "do you understand the basic building blocks that come up over and over again" more then "is your research groundbreaking and new." Asking about techniques to do the above at scale also ups the difficulty of the interview.