Responsibilities Use predictive/statistical modeling and related methods to build world-class, scalable models that will provide high business value. Apply advanced statistics and data mining techniques to analyze and make insights from big data, such as historical production data and simulation/experiment results. Create computer simulations to support operational decision-making. Identify areas with potential for improvement and work with internal teams to generate requirements that can realize these improvements. Create software operational machine learning systems to integrate with commercial software.Basic Qualifications Master’s degree in statistics, mathematics or computer science or minimum 7 years’ equivalent job experience Minimum 7 years’ experience in one or more of the following:o statistical programming language (preferably Python, Scala, R or MATLAB/Octave),o data management (SQL, ETL , Data Factory, data warehouse, etc.) and using databases in a business environment with large-scale, complex datasets, o developing predictive/machine learning models. Strong English verbal/written communication (CET 6 or equivalent experience) & data presentation skills, including an ability to effectively communicate with both business and technical. Experience with large scale analytics paradigms (Map Reduce, NoSQL). Knowledge with supervised learning methods (linear and logistic regression, generalized linear models, decision trees, random forests, support vector machines, graphical models, neural networks, anomaly detection etc.). Knowledge with unsupervised learning methods (K-means, hierarchical clustering, association rules, principal components, etc.). Very strong self-learning skills. Ability to pick up and adapt modeling methods from other disciplines or leverage methods from other skilled colleagues in other departments in solving problems. Strong organizational, time management, communication, and engineering skills are necessary.

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