The two cultures argument is strong, but so is David Hand's "Illusion of Progress" paper [1] which fits into the debate by highlighting that classifier technolo

The two cultures argument is strong, but so is David Hand's "Illusion of Progress" paper [1] which fits into the debate by highlighting that classifier technologies often touted by machine learning aficionados does not scale to better performance when analyzed carefully. In my experience, improvements in performance tend to come from improvements in domain knowledge. This usually plays out in feature selection, but may involve radical changes in the classifier/model design. The secondary ones are more fun and can more easily be the basis of a PhD. The initial ones are usually swept under the rug when they're brutal and boring, though fundamental changes also form great PhDs. I think it's fairly clear that the success of NLP almost entirely came down to a feature engineering change. I don't know what the ideal Data Scientist/Statistician position is. I think it varies based on many of the implicit needs that only statistics savvy businesses can pick between clearly. The role may require strong programming skills, the ability to build and iterate massive multilevel models of social data, a familiarity on-line classifiers, the ability to graph things nicely in R and D3, the ability to from scratch write a non-linear optimizer over a completely novel space, a familiarity with NLTK, or, finally and perhaps most commonly, the ability to pander bullshit about the benefits of machine learning. I love the field and that's why I tend to side with the statisticians who want to really figure out what's needed to do an objectively good job drawing inference from data. Many in the new generation of statisticians are quite successfully crossing the cultural divide. A smaller cohort are brushing up at least their R coding skills to a suitable degree. Some already have an outside competency in programming. I'd like these people to eventually form the core of what real "data science" is. [1] http://arxiv.org/pdf/math/0606441.pdf http://arxiv.org/pdf/math/0606441.pdf