Degree in quantitative field (e.g. Computer Science, Engineering, Mathematics, Statistics, Operations Research or other related field) 3 years of industry or graduate research experience solving analytical problems and building models using quantitative, statistical or machine learning approaches Proficiency with statistical concepts and mathematical models Proficiency with Machine Learning concepts, supervised and unsupervised algorithms Proficiency with Deep learning frameworks and transfer learning models Experience with data visualization libraries such as Matplotlib, Pyplot, ggplot2 Domain expertise and respective business usecases Good Scripting and programming skills in both R and Python Teaching Experience with Machine Learning, Statistics, or other data analysis tools and techniques Teaching Experience performing data extraction, cleaning, analysis and presentation for medium to large datasets Teaching Experience with at least one programming language (i.e. Python, Java, or C+) Teaching Experience with scientific computing and analysis packages such as NumPy, SciPy, Pandas, Scikitlearn, dplyr, or ggplot2 Teaching Experience with statistics methods such as forecasting, time series, hypothesis testing, classification, clustering or regression analysis Teaching Experience with machine learning libraries and packages such as PyTorch, Caffe2, TensorFlow, Keras or Theano Effective communication skills with good voice base