Data Scientist – Digital NT


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**Data Scientist – Digital NT****YOUR TASKS AND RESPONSIBILITIES**The primary responsibilities of this role, Data Scientist – Digital NT, are to:+ Independently design, develop and execute QTL discovery projects through GWAS and bi-parental mapping experiments in support of global Plant Health teams;+ Assume leadership for QTL discovery projects;+ Provide technical contributions in a fast-paced team environment to accelerate our efforts on building an analytics-driven product Pipeline;+ Independently perform statistical analysis, computer programming, predictive modeling and experimental design;+ Contribute to the development of code, bioinformatic analyses pipelines, computational tools and databases for mining and visualizing large genomic data sets;+ Understand the consequences of the different business outcomes, requires little guidance and independently design experiment;+ Demonstrate a moderate level of autonomy in building relationships and networks within current function;+ Develop powerful methodologies to provide alternative solutions in solving complex problems;+ Understand the basic questions being asked in the given scientific domain and applies integrated consultancy skillset to collaborate with subject matter experts and key stakeholders.**WHO YOU ARE**Your success will be driven by your demonstration of our LIFE values. More specifically related to this position, Bayer seeks an incumbent who possesses the following:Required Qualifications:+ Bachelors degree with at least five years of experience or Masters degree with at least two years of experience or Ph.D.;+ Educational preparation or applied experience in Machine Learning, Statistical Genetics, Statistics, Biostatistics, Bioinformatics, Genomics, Computational Biology or other related quantitative discipline;+ Demonstrate intermediate proficiency in computational skills and level of experience building data models using R, Python or other statistical and/or mathematical programming packages;+ Intermediate proficiency in machine learning algorithms and concepts;+ Experience in successful delivery of valuable analysis through application of domain knowledge; evidence of ability to strong business acumen;+ Strong communication competencies to include presentations and delivery of complex quantitative analyses in a clear, concise and actionable manner to extended team and small groups of key stakeholders.**Location:**United States : Missouri : Chesterfield**Division:**Crop Science**Reference Code:**240450

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