Vegetable R&D Intern

At Bayer we're visionaries, driven to solve the world's toughest challenges and striving for a world where ,Health for all, Hunger for none' is no longer a dream, but a real possibility. We're doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining 'impossible'. There are so many reasons to join us. If you're hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there's only one choice.

Vegetable R&D Intern 

YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role, Vegetable Research and Development Intern, are to: 

• The intern will work with a cross-disciplinary team to analyze phenotypic data and develop analytical workflows to draw inference on optimal testing strategy via prescriptive phenotyping;
• The intern will work with data scientist and plant breeders to understand breeding/testing objectives and provide data-driven prescriptions on how to design experiments such as RCBD and/or non-RCBD;
• Daily work will consist of coordinating between field testing partners and data science experts to provide protocol refinement in order to generate actionable phenotyping strategies.
• The intern will drive data analysis and development of visuals for communication of results to partners across multiple domains and to enable future investment decisions;
• The intern will work closely with his/her mentor to deliver results as the end of the internship on the results and next steps.

WHO YOU ARE                                         

Your success will be driven by your demonstration of our LIFE (Leadership Integrity Flexibility Efficiency) values. More specifically related to this position, Bayer seeks an incumbent who possesses the following:

Required Qualifications:

• Bachelor of Science and be pursuing advanced degree (Master of Science or Ph.D.) in Data Science, Statistics, Breeding, Agronomy, Horticulture, Engineering, or related field;
• Experience with working in a cross-disciplinary team of researchers and communicating complex approaches or analysis to such teams;
• Solid understanding of advanced statistics including mixed effect models, experimental design, exploratory data analysis methods, causal inference and machine learning;
• Strong experience with R, Python, or other languages;
• Highly motivated, self-driven and team player.