One year of experience must include i conducting exploratory data analysis and modeling; ii Bayesian method, data visualization, hypothesis testing, power analysis, predictive modeling using linear and nonlinear multiple regression methods, logistic regression, generalized linear models GLM, factor and principal component analysis, decision trees, random forest, gradient boosting; iii using probabilistic programming languages, such as Tensorflow probability, Stan, Pyro, PyMC3 or JAGS; iv R, Python, SQL, PySpark or SparklyRSparkR for building data pipelines andor machine learning models; v working with Cloud computing platforms including Domino, GCP andor AWS; vi distributed version control e.g., gitlab, github, bitbucket and source code management functionality; vii explaining complicated statistical models and theories to nontechnical audiences; and viii documenting detailed and clear project analysis process. Note Since there is no experience required in H.6, the Kellogg language is not required.
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