Genomics & Breeding Data Scientist Intern

Iowa Ag; Johnston, Northern (IA, KY); Hybrid; USD 34,000 - 48,000; Full time

Job summary

Vylor in Johnston, IA is seeking a Breeding Data Scientist Intern to apply data science skills to plant breeding and quantitative genetics. You will work with breeders, statisticians, and domain experts to improve strategies and accelerate genetic gain.

In this role you will analyze phenotypic and genomic data in R or Python, design breeding experiments, and communicate findings clearly to diverse audiences while building reproducible analyses.

Qualifications

• MS or PhD student in Plant Breeding, Quantitative Genetics, Agronomy, Data Science, or related fields.
• Experience with phenotypic and genomic datasets in R or Python.
• Experience with statistical modeling, machine learning, or simulation in biological systems.
• Interest in translating breeding/genomics needs into usable software/tools.
• Interest in building collaboration and communication skills in cross-functional teams.

Responsibilities

• Design, build, and evaluate breeding strategies using quantitative methods.
• Navigate genomic, phenotypic, and environmental data to improve selection decisions.
• Collaborate with breeders to translate data into actionable decisions.
• Communicate findings clearly to scientists and stakeholders.

Skills

Problem solving

Data analysis

R

Python

Statistics

Machine learning

Communication

Team collaboration

Education

MS/PhD student in Plant Breeding, Quantitative Genetics, Agronomy, Data Science, or related fields

Tools

R

Python

Shiny

Git

Job description

Vylor in Johnston, IA is seeking a Breeding Data Scientist Intern to apply data science skills to plant breeding and quantitative genetics. You will work with breeders, statisticians, and domain experts to improve strategies and accelerate genetic gain.

In this role you will analyze phenotypic and genomic data in R or Python, design breeding experiments, and communicate findings clearly to diverse audiences while building reproducible analyses.