[laughfactory]

I agree. A smart data scientist doesn't waste their time reinventing the wheel: they build off the hard work of others. When necessary they can create what is needed, but they don't do so typically.

They are both more and less, in my experience, than statisticians (more flexible and solution-oriented, less rigorous and classical), than analysts (they can do more, in general, but a great analyst will be better at analysing and visualizing), than developers (they know more stats, less software engineering, and have great patience for wrestling data into submission). I like to think of data scientists as people who combine the skills of all the above to solve hard problems which exceed the domain of any of specialty (analyst, statistician, developer). It doesn't mean we're amazing at everything, just that we are effective, flexible problem solvers.

And for the record, machine learning, statistical modeling, and data mining are just a small portion of the pie. Being good at modeling and machine learning will not remotely guarantee success as a data scientist.

[thinkr42]

I respectfully disagree. While I understand where you're coming from, I don't agree with your distinction between an analyst and a scientist. Given the data scientist's typical compensation and expected experience, there should be a higher bar set for them that does include developing solutions from base. I understand the use of utilities, but far too frequently I find people who rely on packages to do their work don't really understand what they're working on (they often don't realize the underlying assumptions that the package writers made for them either). With your description of the tasks for a data scientist, I would label this as a Data Analyst's work if I was hiring one.

I could of course be wrong and have a bit too narrow of a view from my particular subfield.