Agronomic Data Science & Pathology Intern

Join Vylor—Powering the Future of Agriculture​

At Vylor, we’re advancing agriculture through breakthrough science—combining elite germplasm, cutting-edge biotech, and next-generation expertise in gene editing and molecular breeding. Our portfolio includes trusted industry brands like Pioneer®, Brevant®, and Hoegemeyer®, delivering performance farmers rely on.​

Join us to help scale innovation globally and shape what’s next in crop science.

Vylor is seeking an Agronomic Data Science & Pathology Intern to join our fast-paced research & development team that is using leading edge technologies to advance software-based agronomic solutions for growers around the globe. As an intern at Vylor, you will have a unique opportunity to learn, grow, and expand your knowledge as you help research and develop the digital crop advisor of tomorrow. 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. Strong applicants will have completed courses or projects involving data science and/or statistical analysis and modelling. Affinity with agriculture, pathology, epidemiology and biological systems is an advantage.

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

Skills You'll Need:

• Enrollment in a Masters or Doctoral degree program in mathematics, statistics, plant pathology, data science, computer science or related agricultural engineering field is preferred
• 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
• Not required, but preferred technology experiences: Numpy, Pandas, Sklearn, TensorFlow, Keras, Matplotlib, Kubernetes, Amazon Web Services (AWS), RESTful API Services

Are you a good match? Apply today! We seek applicants from all backgrounds to ensure we get the best, most creative talent on our team.

Corteva announced to separate our current advanced seed and genetics business to establish a standalone company, operating as Vylor, Inc. The process of the planned separation is anticipated to be completed the fourth quarter of 2026, subject to legal requirements and board approval. Should you accept this position, it is anticipated that, following conclusion of the separation, you would be an employee of Vylor, Inc. and your employment would be governed by Vylor, Inc. employment processes, programs, policies, and benefit plans. In that case, details of any planned changes would be provided to you by Vylor, Inc. at an appropriate time and subject to any necessary consultation processes.

Vylor is an equal opportunity employer. We are committed to embracing our differences to enrich lives, advance innovation, and boost company performance. Qualified applicants will be considered without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, military or veteran status, pregnancy related conditions (including pregnancy, childbirth, or related medical conditions), disability or any other protected status in accordance with federal, state, or local laws.