YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role, Data Scientist - Phenomics, 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:

• Bachelor’s degree with at least five years of experience or Master’s degree with at least two years of experience or Ph.D.;
• Educational preparation or applied experience in at least one of the following areas: Machine Learning, Electrical/Industrial Engineering, Operation Research, Statistical Genetics, Statistics, Biostatistics, Bioinformatics, Genomics, Computational Biology, Applied Mathematics, Computer Science, Environmental Science/Engineering, or other related quantitative discipline;
• Demonstrates intermediate proficiency in computational skills and level of experience building data models using R, Python or other statistical or mathematical programming packages;
• Strong proficiency in predictive modeling—to include comprehension o 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.