For example I couldn't implement an SVM library from scratch to save my life, but I do understand what it means to be a 'maximum margin' classifier, from a high level how the 'kernel trick' works, and why you would tune regularization and cost parameters. However this knowledge has been enough to help me in quite a few interesting problems.
Reading accounts of how others have solved real world data mining issues it's amazing how often a very simple model will do the job, and also how often, even among more serious researches, there's a bit of intuition in finding the right combination of parameters, and lots of trial and error in searching for which model/blend of models really does the job.
I think there's a lot of room for more people approaching data mining with the 'hacker' mentality. Sure you don't want 'data scientists' using a randomForest whose eyes glaze over when you mention the word "ensemble", or someone who couldn't explain in plain terms what a "maximum margin hyperplane" is. But, there is a growing space for practitioners in this space, that aren't necessarily as strong in the theory as people working in the pure research space.