SPD Data Scientist 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.
Are you the kind of scientist who looks at a result and asks "what is this really telling us, and what do we do differently tomorrow?" Do you like taking a messy, open-ended question and turning it into an answer someone can act on? If so, you might be a great fit for our Breeding Data Scientist Intern position.
Vylor isseeking a graduate student who wants to apply data science and quantitative omics skills to real decisions in a commercial plant breeding program. You will work alongside breeding analysts, statisticians, and breeders on a focused project with a clear deliverable — something that informs how we run our pipelines, not a study that ends on a shelf.
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, develop, and evaluate breeding strategies that leverage both traditional and modern quantitative methods.
- Lead a project by helping frame the question, choose the approach, run the analysis, and land a recommendation — with support and feedback along the way, but with real room to drive it yourself.
- Learn to navigate genomic, phenotypic, environmental, and/or other types of data within breeding pipelines to improve selection accuracy and decision-making.
- Interact with breeders to understand how technologies affect breeding decisions and understand how your own results can impact in the field.
- Communicate findings and recommendations clearly to technical and non-technical 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.
- A bias toward application: comfortable choosing a method that is good enough to answer the question and judging your work by the decision it improves rather than by its sophistication.
- 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 applied to biological systems.
- Interest in building collaboration and communication skills while working in cross-functional teams.
What will set you apart:
- Intellectual boldness: willingness to question an assumption, propose an unconventional approach, and defend it with evidence — as well as the judgment to know when to drop an idea that is not working.
- Creativity in both directions: in how you interpret data and results, and in how you build the algorithm or workflow that gets you there.
- Hands-on experience with genomic selection, genetic algorithms, environmental or omics 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.
- Proactive and self-directed — you identify what needs doing next, ask for what you need, and move forward without waiting to be told.
- A passion for applying science to solve real-world agricultural challenges.
Location: Johnston, IA (preferred; other locations may be available)
Duration: 3 months Summer 2027 internship, with the option to extend up to a total of 6 months if there are mutual interest and availability
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.