Skills required Experience developing predictive models using statistical and machine learning techniques neural networks, gradient boosting algorithms, and tree methods. Experience with statistical time series analysis and time series modeling including time series forecasting and anomaly detection using AR Models, Markov Models, and Neural Network Models. Experience coding, debugging, and maintaining code in Python. Experience using Pandas for data manipulation. Experience using NumPy and SciPy for numerical processing. Experience using matplotlib and seaborn for visual presentations. Experience composing structured, concise, and efficient queries using SQL, Hive, and Spark to query data for analysis. Experience analyzing data and gaining insights using statistical techniques confidence intervals, hypothesis testing, AB testing, and parameter estimation. Experience developing, training, and testing deep learning neural network models in TensorFlow. Demonstrated knowledge of optimization theory and the numerical method techniques to be used for optimal resource allocation. Experience applying research results from the area of machine learning and statistics and transmitting research concepts to technical and nontechnical audiences. Experience using Git for code version control and collaboration. Experience using document processing and presentation tools including Microsoft PowerPoint, Microsoft Excel, and LaTeX. Employer will accept any amount of graduate coursework, graduate research experience or professional experience with the required skills.br br Continuation of Section H.4B Major field of study Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, Engineering any, or related field.br br Continuation of Section H.10B acceptable alternate occupation Required experience experience in an analytics related field.
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