Strong knowledge of advanced statistical methods, Bayesian learning techniques, pattern recognition and outlier detection algorithms, as well as advanced topics such as decision trees, random forests, neural networks and other deep learning methods.
Familiarity with one or more script-based data visualization tools, such as ggplot, matplotlib, Shiny, or D3
Knowledge of one or more machine learning or statistical modeling tools such as R, SAS, MATLAB, or Python (scikit-learn, Theano).
Proficiency in computer processes and methods, and familiarity with languages such as C/C++, Java, C#, SQL, Perl, Python; familiarity with UNIX, shell scripting, distributed/parallel computing, a scripting language such as Python or Perl or similar; familiarity with regular expressions; familiarity with state-of-the-art database techniques.
Hands-on technical experience with conceptualizing large scale data solutions, such as - Hadoop, Teradata, Sybase IQ, Microsoft Analytics Platform System (Client), IBM Netezza, etc. preferred
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