Agronomic Data Science & Pathology Intern

Vylor is advancing agriculture through crop science, biotechnology, gene editing, and digital agronomic solutions. The Agronomic Data Science & Pathology Intern will research and develop software-based crop advisory solutions by modeling agricultural and weather data, analyzing plant disease and pest management, developing Python code, and collaborating with research and engineering teams.

What you'll do:

• Model, integrate, and analyze agricultural and weather data
• Plant disease and pest management modeling in crops such as corn, soybeans and canola, etc
• Develop and execute Python code in high performance distributed Unix/Linux computing environments
• Work collaboratively on agile research teams to create innovative software solutions for growers
• Design, develop, and support a variety of high-performance software solutions for R&D
• Continuously learn and share your technical knowledge with key leaders and project stakeholders

What they're looking for:

• Experience with plant pathology and coding in python is essential for this position
• Applicants should also have a drive for excellence, excel in using creative approaches to solving complex problems, and possess an innovative mindset
• Affinity with agriculture, pathology, epidemiology and biological systems is an advantage
• * 3.5+ current cumulative GPA
• * Excellent problem-solving skills using creative approaches
• * Hands-on experience with python, data analysis and statistics is required
• * Relevant experience using machine learning and mechanistic modelling approaches to solve complex problems with mixed variable datasets
• * Domain knowledge of plant pathology, epidemiology and biological systems
• * Ability to work effectively with cross-functional science and engineering teams and business partners
• * Enrollment in a Masters or Doctoral degree program in mathematics, statistics, plant pathology, data science, computer science or related agricultural engineering field is preferred
• Strong applicants will have completed courses or projects involving data science and/or statistical analysis and modelling
• * Not required, but preferred technology experiences: Numpy, Pandas, Sklearn, TensorFlow, Keras, Matplotlib, Kubernetes, Amazon Web Services (AWS), RESTful API Services