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.
