YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role, Data Scientist Genomic & Environmental Dynamics, are to:

• 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;
• Build cross-functional relationships to collaboratively partner with the business and effectively network within the Data Science Community;
• Use advanced mathematical models, machine learning algorithms, operations research techniques, and strong business acumen to deliver insight, recommendations, and solutions;
• Develop sustainable, consumable, accurate, and impactful reporting on model inputs, model outputs, observed outputs, business impact, and key performance indicators;
• Present compelling, validated stories to all levels of organization, including peers, senior management, and internal customers to drive both strategic and operational changes in business.

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:

• Minimum of a Bachelor’s degree with 5+ years experience, a Masters degree with 2+ years relevant experience or PhD;
• Educational preparation or applied experience in at least one of the following areas: Machine Learning, Electrical/Industrial Engineering, Plant Physiology, Statistical Genetics, Biostatistics, Bioinformatics, Genomics, Computational Biology, Applied Mathematics, Computer Science or other related quantitative discipline;
• Demonstrates intermediate proficiency in computational skills and level of experience building data models using R, Python or other statistical and/or mathematical programming packages;
• Demonstrates basic understanding of software development best practices (Version Control, Code Documentation & Review, Cloud Based Sequence Analysis, Database Management);
• Strong proficiency in predictive modeling—to include comprehension of theory, modeling/identification strategies and limitations and pitfalls;
• 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.

Relocation may be offered for this role.