At Corteva Agriscience, you will help us grow what’s next. No matter your role, you will be part of a team that is building the future of agriculture – leading breakthroughs in the innovation and application of science and technology that will better the lives of people all over the world and fuel the progress of humankind.
Are you passionate about advancing plant breeding through innovative science and technology? Do you thrive at the intersection of fieldwork and analytics, and enjoy turning complex biological questions into practical breeding solutions? If so, you might be a great fit for our 3-month Breeding Data Science Intern position.

We are seeking a graduate student with an interest in applying data science skills in plant breeding and quantitative genetics. In this role, you will interact with breeding analysts, statisticians, and domain experts to enhance breeding strategies and accelerate genetic gain.

At Corteva Agriscience™, we are committed to transforming agriculture through innovation. Join us in shaping the future of plant breeding and delivering improved varieties to farmers around the world.

What You'll Do:

Partner closely with scientists in the North America Technology Deployment team to design, build, and evaluate breeding strategies that leverage both traditional and modern quantitative methods.Learn to navigate genomic, phenotypic, and/or environmental data within breeding pipelines to improve selection accuracy and decision-making.Interact with breeders to understand how technologies affect breeding decisions.Communicate findings and recommendations clearly to relevant audiences

What Skills you Need:

Pursuing MS or PhD in Plant Breeding, Quantitative Genetics, Agronomy, Data Science, or related fields.Excellent problem-solving skills and the ability to explore analytic solutions by asking the right questions, analyzing data, and producing actionable results.Experience working with phenotypic and genomic datasets in R or Python for data analysis and visualization.  Experience with genomic selection, genetic algorithms, or environmental modeling in a breeding context is a plus.Familiarity with statistical modeling, machine learning, or simulation techniques as applied to biological systems.Interest in building collaboration and communication skills, while working in cross-functional teams.