Initially I was in the camp of reluctant to associate myself with "data science" because it might be just a passing fad. It still might be, but for now I have started using that label.

I started in industry as "an applied or pragmatic statistician" that is someone trained in social science research with a strong quantitative methodology bias. As I went along I added focus group moderation, in-depth interviewing, competitive analysis, ROI analysis and strategy consulting... so I stared calling what I do "Research-based Consulting."

But that label doesn't seem to quite capture building taxonomies and text indexing systems or doing latent semantic analysis. Nor does that "Research-based Consulting" capture teaching myself web development in order to create data-focused web applications. And, what about all the database work that I do in operational systems? Or, how do I fit in things like managing and validating data collection and aggregation systems that track prices for ~10K sku's across multiple retail websites, combine them in a weighted algorithm that reflects my client's business priorities and drives thousands of automated transactions every day?

So even though I came from a background with a lot of grad level statistical training and even at one point somewhat identified as a statistician it feels like current definitions of "data scientist" captures more of what I actually do. So I have come to be at peace with the term.

I totally agree with the points in the article about a mult-disciplinary team. I would love to recruit people who are better than me at each sub-discipline and figure out how to help them work together.