ob Requirements

• Bachelor’s or Master’s or PhD degree in Science, Engineering, Computer Science, Applied Math / Statistics, or a closely related field
• Demonstrated ability to solve challenging data science problems by applying or adapting existing computational methods is required.
• Python Fluency of the Data Science Stack (scikit, pandas, numpy, scipy, statsmodels, matplotlib).  Other scripting languages are possible (R, Julia, Matlab)
• General Statistical/Machine Learning – supervised, unsupervised, and reinforced learning
• Specific Techniques: K-Means, Hierarchical Clustering, PCA (Principal Component Analysis), Truncated SVD(Singular Value Decomposition), Naïve Bayes, Regularization (LASSO, Ridge, Elastic Net), Support Vector Machines, Decision Trees, Random Forest, XG-Boost, Boosting, Voting classifiers, cross validation, etc.
• Software Development Skills (unit testing, debugging, git/subversion)
• Minimum of 1-3 years solving problems using tools and techniques described below
  • Programming Languages (C/C++, R, Java, Julia, and functional languages)
  • Natural Language Processing (NTLK, TFIDF, W2V, Gensim, Tika, CoreNLP)
  • Deep Learning Experience (Keras, Tensorflow, Theano, others)
  • Computer Vision Experience (OpenCV, convolution, digital filtering)
  • Time Series Analysis (Fourier techniques, cuFFT, fftw, ARIMA models)
  • Parallel Processing (OpenMP, MPI, PVM, CUDA, OpenCL, etc.)
  • Web Development (HTML, CSS, Flask, Django, NodeJS, D3JS)
  • Database Experience (SQL Server, Oracle, MySQL, MariaDB, MongoDB, Cassandra)
  • Big Data Experience (Hadoop, Spark, MapReduce, HBase, Hive, Pig, Sqoop)
  • Visualization Environments (Tableau, Spotfire, Splunk, IDL, PVWave, PowerBI)
• Should be collaborative, efficient, responsible, creative, flexible
• Should have a safety oriented mindset
• Proficient in English (written & verbal)
• Team player & should demonstrate positive attitude
• Focus on customer
• Analytical problem solver
• Goal oriented & should be good with time management