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Position: Statistical Scientist Co-op
Institution: Bayer AG
Location: Chesterfield, Missouri, United States
Duties: Explore data analysis and Q Cing of large volumes of relevant weather, soil, management, and product performance data from various platforms (field, controlled environment, lab) using appropriate statistical and data science techniques; Develop and validate models for selecting representative testing locations for developing biological product portfolios; Use advanced statistical methodologies/models and machine learning approaches to understand and predict how complex microbial x environment and management practices (MEM) interaction effects impact the performance and stability of key biological products in the pipeline
Requirements: Currently enrolled in a Master’s degree or a Ph.D. program pursuing a degree in Statistics, Data Science, or a related field; Experience and knowledge in applying advanced statistical modeling approaches such as generalized linear mixed models, multivariate statistics, experimental design concepts, etc; Gain insights from large and diverse data sets; Applied knowledge of machine learning algorithms and methodologies to develop predictive models to drive insights and decisions
   
Text: Statistical Scientist Co-op Explore data analysis and Q Cing of large volumes of relevant weather, soil, management, and product performance data from various platforms (field, controlled environment, lab) using appropriate statistical and data science techniques; Develop and validate models for selecting representative testing locations for developing biological product portfolios; Use advanced statistical methodologies/models and machine learning approaches to understand and predict how complex microbial x environment and management practices (MEM) interaction effects impact the performance and stability of key biological products in the pipeline Currently enrolled in a Master’s degree or a Ph.D. program pursuing a degree in Statistics, Data Science, or a related field; Experience and knowledge in applying advanced statistical modeling approaches such as generalized linear mixed models, multivariate statistics, experimental design concepts, etc; Gain insights from large and diverse data sets; Applied knowledge of machine learning algorithms and methodologies to develop predictive models to drive insights and decisions
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