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predictive analytics. Regression, Time Series, XGBoost, Random Forest, SVM, Naive Bayes (standard Scikit learn models). Data Robot experience a plus. Demonstrated ability to express complex analytical and
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to collect and analyse (Frequentist, Bayes, mixed-effects) all forms of data gathered and will have experience with contemporary packages (R-studio) Have skills in experimental and analytical design Be able
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, and Cass (now Bayes) Business School, and at the Federal Reserve Board (Washington), Federal Reserve Bank of New York, Federal Reserve Bank of San Francisco, Goldman Sachs, Cornerstone Research, and in
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• Flexibility to learn new technologies, APIs, and SDKs by reading documentation • Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc
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learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc. • Experience with common data science toolkits, such as R, Weka, NumPy, MATLAB, etc. Excellence in at least one of
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, APIs, and SDKs by reading documentation • Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc. • Experience with common data
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confronting data quality and explore the common approaches and tools for resolving them. Advanced data analytics methods, such as advanced decision trees, naive Bayes, k-nearest neighbors, and neural networks
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strong pedagogical and linguistic competence for EMI. Where to apply Website http://sig.ufabc.edu.br/sigrh/public Requirements Research FieldOtherEducation LevelPhD or equivalent Skills/Qualifications
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, Data Warehouses, Data Mining Machine Learning, Data Visualization, Modeling Statistics: Correlations, Multivariate Linear Regression, Linear Regression, Naïve Bayes & Decision Tree Clustering Our Culture
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temporal drift using mixed-effects models, empirical Bayes approaches, and sensitivity analyses. Handle missing with principled methods (e.g., MICE, IPW); quantify robustness. Maintain privacy-conscious data