In Bayer’s Crop Science division, we shape agriculture through breakthrough innovation that helps to nourish our growing world, preserve natural resources and deliver better solutions for all farmers. Using the creative spark that comes from human ingenuity, we seek to deliver world-class innovation, set new standards in sustainability, and drive our digital transformation. Our products include high-performance seeds & traits, crop protection solutions, and digital farming tools.

YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role, Protein Design Data Scientist Intern, are to: 

• Provide technical contributions in a fast-paced team environment to accelerate our efforts on building an analytics-driven product pipeline;
• Use advanced mathematical models, machine learning algorithms, and protein modelling methods;
• Develop sustainable, consumable, accurate and impactful reporting on model inputs, model outputs, observed outputs and key performance indicators;
• Form partnerships with cross functional teams to drive novel protein engineering insights;
• Present compelling, validated stories, including peers and internal customers to communicate research findings;
• Leverage protein structure knowledge and machine learning methods to deliver new insights into the pipeline.

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:

• Currently enrolled in a Graduate degree program;
• Educational preparation or applied experience in at least one of the following areas, Machine Learning, Biostatistics, Bioinformatics, Genomics, Computational Biology, Applied Mathematics, Computer Science, Chemistry or other related quantitative discipline;
• Demonstrates basic computational skills and level of experience using R, Python or other statistical and/or mathematical programming packages;
• Entry level proficiency in predictive modeling;
• Entry level proficiency in machine learning algorithms and concepts.

Preferred Qualifications:

• Knowledge of protein structure and protein engineering software.