The data is not enough: Creative data scientists make the difference

True, but intuition and general analytical ability is often overlooked when hiring data scientists -- because the term is so broad, what people are often looking for is a mixture of statistical know-how and programming ability, but often forget to ask for a good understanding of econometric principles.

For instance, there is a difference between understanding generalization error in the statistical/machine learning sense, and generalization error in the sense of external validity. You'd be surprised at how many models, even at reputed companies, use features that do not make sense or are based on implicit assumptions about the studied population. Obviously insight is always a good quality to have, but it is particularly critical for data scientists as the right or the wrong insight in the context of machine learning algorithms can have huge consequences on your company.