Skills hard to find in machine learners?

Just knowing about techniques is akin to knowing the animals in a zoo -- you can name them, describe their properties, perhaps identify them in the wild. Understanding when to use them, formulating, building, testing, and deploying working mathematical models within an application area while avoiding the pitfalls --- these are the skills that distinguish, in my opinion. The emphasis should be on the science, applying a systematic, scientific approach to business, industrial, and commercial problems. But this requires skills broader than data mining & machine learning, as Robin Bloor argues persuasively in "A Data Science Rant". Application areas: learn about various application areas close to your interest, or that of your employer. The area is often less important than understanding how the model was built and how it was used to add value to that area.