Corteva-posted about 1 year ago
Full-time • Intern
Johnston, IA
Chemical Manufacturing

The Agronomic Data Science & Engineering Intern at Corteva Agriscience will join a dynamic research and development team focused on creating innovative software solutions for agricultural challenges. This internship offers a unique opportunity to engage in hands-on projects involving data modeling and analysis, particularly in the context of plant disease and pest management. The role emphasizes the use of Python programming and collaboration within agile teams to advance digital crop advisory technologies.

  • Model, integrate, and analyze agricultural and weather data.
  • Work on projects related to plant disease and pest management modeling in crops such as corn, soybeans, and canola.
  • Develop and execute Python code in high-performance distributed Unix/Linux computing environments.
  • Collaborate on agile research teams to create innovative software solutions for growers.
  • Design, develop, and support a variety of high-performance software solutions for research and development.
  • Continuously learn and share technical knowledge with key leaders and project stakeholders.
  • Current enrollment in a master's or doctoral degree program in mathematics, statistics, plant pathology, data science, computer science, or a related agricultural engineering field.
  • Must be enrolled in classes the semester following their internship with Corteva at a US-accredited institution.
  • 3.0+ current cumulative GPA.
  • Must be able to work full-time (40 hours per week) in Indianapolis, IN for 10-12 weeks during the internship (typically May to August).
  • Excellent problem-solving skills using creative approaches.
  • Hands-on experience with Python and data analysis/statistics is required.
  • Relevant experience using machine learning and mechanistic modeling approaches to solve complex problems with mixed variable datasets.
  • Ability to work effectively with cross-functional science and engineering teams and business partners.
  • Experience with Numpy, Pandas, Sklearn, TensorFlow, Keras, Matplotlib, Kubernetes, Amazon Web Services (AWS), RESTful API Services.
  • Hands-on experience in a fast-paced research and development environment.
  • Opportunity to work on real-world agricultural challenges.
  • Mentorship from experienced professionals in the field.
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