The position requires a Masters degree, or foreign equivalent, in Statistics, Mathematics, Economics, Actuarial Science, or a related scientific field plus two 2 years of experience in the job offered or a Analyst II, Data Sciencerelated occupation. Position also requires demonstrable experience with each of the following Data preparation, including data extraction and manipulation skills, EDA Exploratory Data Analysis, transformations; Applying quantitative analysis and predictive modeling, especially generalized linear models, including Logistic regression, Poisson, and Gamma; Regularization in regression models including Lasso, Ridge, and Elastic net; Model evaluation, validation, testing, and deployment; Demonstrable understanding of the theory of model validation methods, including gain chart, liftdual lift chart, confusion matrix, precision, recall, f1 score, and cross validation; Machine learning methodologies including Support Vector Machine, Neural Network, Random Forest, Gradient Boosting Machine, Linear and Quadratic Discriminant Analysis, and Nave Bayes; Clustering methodologies including Kmeans clustering and meanshift clustering; Statistical methodologies for timedependent data, specifically survival analysis; Statistical inference including probability distribution, and probability model parameter estimation; Optimization methodologies including Newtons methods, Quasi Newton methods, and Coordinate descent; Bayesian Statistics methodologies; Black box model interpretation methods including Sharpley value, and Partial dependency plot; Predictive analytics tools, including R and SAS or a related technology; Programming languages including Python and SQL; Building Python model automation tools package; Working with cloud based environments, specifically Amazon Web Service; Advanced functions in Excel, including vlookup and pivot; and Distributed project version control Git. Travel required up to 15. Telecommuting permitted up to 20.
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