Comment by Vincent Granville on February 15, 2013 at 10:23am

A data scientist is a bit of everything (statistician, software engineer, business analyst, computer scientist, six sigma, consultant, communicator), but most importantly she is a senior analytic practitioner

• with a very good sense for business data and business optimization at large. 
• knowledge of big data - both drawbacks and potential (and able to leverage its potential)
• who enjoys swimming in unstructured data, fuzzy non-SQL "joins"
• who knows the limitation of old statistics (regression etc.) yet knows how to correctly do sampling, cross-validation, Monte Carlo simulations, design of experiments, assessing lift, identify good metrics
• who knows the limitations of MapReduce, and how they can be overcome
• who can design and develop robust, simple, efficient, reliable, scalable, useful predictive algorithms - whether or not based on statistical theory

A data scientist may not know much (but at least a little) about linear regression, statistical distributions, the complexity of the quicksort (sorting) algorithm or the limit theorems. Her knowledge of SQL can be a bit elementary, although she can run a big SQL query 10 times faster than business analysts who use tools such as Toad or Brio. Her strengths, skills and knowledge are briefly outlined above.  

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