SPD Data Scientist Intern

Vylor Inc.; Des Moines (IA); On-site; USD 52,000 - 73,000; Full time

Job summary

Vylor, Inc. is seeking a Data Scientist Intern to advance plant breeding through quantitative genetics. You will partner with breeding scientists to design strategies, analyze genomic and phenotypic data, and translate results into actionable recommendations for decision-making.

The role blends field-facing biology with analytics, offering exposure to modern breeding methods, R/Python workflows, and reproducible analyses in a collaborative, fast-paced environment. Johnston, IA location preferred.

Qualifications

• MS or PhD student in Plant Breeding, Quantitative Genetics, Agronomy, Data Science, or related fields.
• Excellent problem-solving skills and 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.
• Familiarity with statistical modeling, machine learning, or simulation techniques as applied to biological systems.
• Interest in translating breeding and genomics needs into practical, usable software or analytical tools.

Responsibilities

• Design, build, and evaluate breeding strategies leveraging traditional and modern quantitative methods.
• Navigate genomic, phenotypic, and environmental data within breeding pipelines to improve selection accuracy.
• Communicate findings and recommendations clearly to scientists and cross-functional partners.

Skills

Data analysis

R

Python

Statistics

Machine learning

Education

MS or PhD student

Tools

Git

Shiny

Jupyter

Job description

Join Vylor—Powering the Future of AgricultureAt 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.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 SPD Breeding Data Scientist Intern position.Vylor is 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 scientists, statisticians, and domain experts to enhance breeding strategies and accelerate genetic gain.At Vylor, 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.In this role, you will: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.Job Qualifications:MS or PhD student 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.Familiarity with statistical modeling, machine learning, or simulation techniques as applied to biological systems.Interest in translating breeding and genomics needs into practical, usable software or analytical tools.Interest in building collaboration and communication skills, while working in cross-functional teams.What will set you apart:Proficiency in R, ideally including experience building interactive applications (e.g., Shiny) or other web-based tools.Experience building reproducible analyses and pipelines (e.g., version control such as Git, workflow automation, or scripting to refresh analyses as new data arrives).Hands-on experience with genomic selection, GWAS/marker-effect estimation, or environmental modeling in a breeding context.Understanding of breeding program design, selection theory, and genetic evaluation methods.Experience applying machine learning and/or artificial intelligence to breeding problems.A passion for applying science to solve real-world agricultural challenges.Location:Preferred location for this position is Johnston, IAAre 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.