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The R using number-cruncher. Can run quick Group By’s and Counts on Numbers in R/Python . This profile is the coding version of Data Analyst from earlier days. Automated report generation in a more analyst-y organization is the most common location one finds this profile in.
Tools Used : R (dataframes), SQL.

The Data Engineer who is also a occassional Data Scientist. Take a library from here, take some code from there and make something good enough while you manage the data pipeline. Very common profile, Data Science tasks include writing programs to automate report generation in Pandas, trying out simple Machine Learning models and (now-a-days) running a pretrained Neural Network on the data
Tools: Python toolchain, Pandas, nltk, Keras.

The tabular ML’er (or the XGBoost specialist). Ardent Kaggler, can train multiple algorithms and stack models and optimize the heck out of them. These guys have deep expertise with running and optimizing standard algorithms like XGBoost, Ridge Regression and (now-a-days) Keras models.
Tools: Python or R, uses XGB, Keras a lot.

The old style ML’er . Close to 4, but not limited to categorical models only. Very good at feature engineering. This was the only Machine Learning expertise until the newer Deep Learning profile came up.
Tools: C++ / Python with Scikit Learn.

Deep Learning Guy. Needs a GPU system and a well tagged dataset and needs to try out architectures and do no feature engineering. Will spend lot of time in trying arcitectures and minimal in feature engineering, but the accuracy will be insane.
Tools: Python, Theano, Tensorflow and high level libraries like Keras.

The domain specialist. Knows a lot about domain, something about linear models. Codes the domain information and trains a linear algorithm on top. Includes mechanical engineers, analysts at different firms and scientists in pure/applied sciences.
Tools: Different Specializations use different things. Matlab by Engineers, C++/Fortran and sometimes R/Python.

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