Analytics Automation Intern – #01RXC

Job Listing No: 9740000

Analytics Automation Intern – #01RXC

If you love developing innovative technologies that create breakthrough products, you???ll want to check out this exciting opportunity in Data Science in our Company.  We are seeking exceptionally talented graduate-level students who share our passion for innovation to be part of our Biotech Data Science & Analytics program at our R&D Headquarters in St. Louis, Missouri. This team has access to data you???ve only dreamed about and is driving creation of predictive and prescriptive models that will shape the industry. We work on hard problems because we love the challenge.  As part of our diverse, highly dynamic group, you will work side-by-side with a team of exceptional data scientists with diverse backgrounds (Statisticians, Mathematicians and Engineers) to foster your career growth while delivering next-generation scientific breakthroughs. Shift from theory-based statistical programming to development of analytics modules that excel in computational efficiency in terms of speed, accuracy, and robustness. This is the transition we expect from the Analytics Automation Intern, who will apply cutting age computational techniques and algorithms to develop R modules for modeling and analysis of data from our agricultural experiments. They will work closely with the members of the Data Science & Analytics team, biotech scientists and members from R&D IT. Dates for this summer internship are May 13 – August 2 of 2019.Qualifications:Candidates must be currently enrolled in university within the U.S. pursuing a Ph.D. degree in Statistics, Biostatistics, Mathematics, Computational Statistics, Computational Mathematics, or a closely related areaProficiency in R programming with experience in development of computationally efficient R packages in statistics/data scienceExperience in interfacing R with high level programming languages such as Python and/or MATLAB to enhance computational efficiencyExperience in application of numerical methods for dealing with large and high dimensional unbalanced data structureKnowledge of Analysis of Variance, General and Generalized Linear Mixed Model, Regression and other related statistical methodologies and the implementation of these techniques in RExperience in machine learning and predictive analyticsStrong communication skills for effective interactions with senior business/R&D stakeholders as well as peer groups and team membersPreferred:Experience in cloud computing and/or parallel computing, e.g. cluster computing or GPU computingExperience with numerical linear algebra for solving linear systems with sparse and ill-conditioned matricesDomain knowledge and training in crop science discipline

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