Masters degree in Computer Science, Engineering Science, Statistics or related field completion of a universitylevel course, research project, internship, thesis, or one year ofbr experience involving the following Knowledge of machine vision traditional machine vision algorithms and modern deep learningbased techniques; Knowledge of advanced techniquesbr of deep learning in neural network architecture development and tuning; Building or applying statistical process control to production lines, designed to improve the yield of manufacturingbr processes or reduce annualized scrap; Completing predictive analytics using machine learning techniques such as decision tree, random forests, KNN knearest neighbors algorithm, orbr SVM Support Vector Machine and maintaining the ability to build recommendation systems and help an organization to forecast the potential future outcomes; Understanding of statisticsbr such as statistical tests, distributions or maximum likelihood estimators and the ability to differentiate and choose appropriate analytic techniques or methods to tackle business questions or resolving imperfections in data Missing values, inconsistent string formatting, fuzzy match, data unstacking, clean, format, or standardize and harmonize operations data for the analysis step; and Cloud computing and file storage, including architecture compliant with regulatory and quality requirements for CGMP current good manufacturing practices with AWS Amazon Web Services.
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