Biostatistics Scientist (Plant Science)

About the Role

The Biostatistics Scientist supports applied statistical analysis, quantitative genetics, and breeding analytics for vegetable crop research programs. This entry-level role contributes to genomic, phenotypic, and field-trial data analysis under the guidance of senior scientists, managers, and cross-functional project teams. The position helps develop reliable, reproducible analytical workflows that improve trait evaluation, marker-assisted selection, genomic prediction, and data-driven breeding decisions.

Responsibilities

• Statistical Analysis, Quantitative Genetics & Genomic Prediction
  • Support the development, testing, and interpretation of statistical and genomic prediction models for vegetable crop breeding programs.
  • Apply standard statistical and quantitative genetics methods, such as mixed models, heritability estimation, genetic correlations, and basic genomic prediction approaches.
  • Assist with evaluating model performance, prediction accuracy, and data quality across populations, environments, and breeding stages.
  • Contribute to analyses that help breeders understand trait variation, experimental results, and selection opportunities.
  • Document methods, assumptions, code, and results clearly to support reproducibility and team review.
• Molecular Marker & Trait Analytics
  • Analyze molecular marker datasets, including SNP and haplotype data, to support trait mapping, marker validation, and breeding decisions.
  • Assist molecular and breeding teams with data summaries for marker development, marker deployment, and trait evaluation projects.
  • Support quality control of genotypic and phenotypic datasets, including data cleaning, formatting, consistency checks, and basic exploratory analysis.
  • Help prepare selection metrics, trait summaries, and visualizations that integrate multiple sources of breeding data.
  • Translate analytical results into concise summaries that can be reviewed by breeders, molecular scientists, and project teams.
• Genomic, Phenotypic & Field Trial Data Analysis
  • Prepare, manage, and analyze genomic, phenotypic, greenhouse, and field-trial datasets under guidance from senior team members.
  • Develop and maintain reproducible scripts for data quality control, statistical analysis, visualization, and reporting.
  • Contribute to the improvement of analytical templates, reporting workflows, and shared data practices in collaboration with bioinformatics and data teams.
• Project Support & Cross-Functional Collaboration
  • Support analytical components of breeding, trait development, molecular marker, and technology projects.
  • Collaborate with breeders, phenotyping, molecular biology, bioinformatics, and data teams to understand project objectives and data requirements.
  • Prepare clear technical summaries, tables, figures, and presentations to communicate results to internal stakeholders.
  • Learn and apply current methods in biostatistics, quantitative genetics, breeding analytics, and reproducible scientific computing.

Requirements

• Education
  • PhD in Biostatistics, Statistics, Quantitative Genetics, Plant Breeding, Computational Biology, Data Science, or a related field; industry experience a plus.
  • or MS in Biostatistics, Statistics, Quantitative Genetics, Plant Breeding, Computational Biology, Data Science, or a related field with 0–2 years of relevant academic, internship, or industry experience.
• Experience & Technical Skills
  • Foundational training in statistics, biostatistics, quantitative genetics, plant breeding, computational biology, or related analytical disciplines.
  • Experience with statistical analysis of biological, genomic, phenotypic, field-trial, or experimental datasets through graduate research, internships, or applied projects.
  • Working knowledge of statistical programming in R, Python, SAS, or similar tools.
  • Good understanding of experimental design, mixed models, regression, data visualization, and reproducible analytical workflows.
  • Experience with molecular markers, genomic data, plant breeding concepts, or trait analysis is desirable.
  • Ability to learn new methods, manage multiple analytical tasks, and deliver accurate results with guidance.
  • Strong attention to detail, scientific curiosity, communication skills, and willingness to collaborate across disciplines.

Preferred Qualifications

• Research experience in plant breeding, seed industry research, agricultural biotechnology, or applied life-science data analysis.
• Experience in genomic prediction, QTL mapping, GWAS, marker-assisted selection, or trait discovery workflows.
• Familiarity with breeding databases, phenotyping systems, laboratory information systems, or integrated data platforms.
• Experience preparing figures, tables, dashboards, or technical reports for scientific or cross-functional audiences.
• Exposure to cloud-based, Linux, Git, or high-performance computing environments for data analysis.
• Interest in applying AI, machine learning, and modern statistical methods to practical breeding and research questions.

Competencies & Behaviors

• Demonstrates curiosity, initiative, and accountability in learning new analytical methods and scientific workflows.
• Applies statistical methods carefully, with attention to data quality, assumptions, and reproducibility.
• Works collaboratively with scientists from breeding, molecular biology, phenotyping, bioinformatics, and data teams.
• Communicates analytical results clearly to both technical and non-technical audiences.
• Manages assigned tasks effectively, asks timely questions, and follows through on deliverables.
• Contributes to a culture of scientific rigor, continuous improvement, teamwork, and practical problem solving.

Reports to Senior Biotech Manager and works under the guidance of senior scientists, project leads, and cross-functional research teams.

Benefits

• Health & Wellness
  • Medical, Dental & Vision Insurance
  • Monthly Wellness Stipend
  • Employee Assistance Program (EAP)
  • Employee Philanthropic Giving Program
  • Disability Insurance (plans vary by location)
• Financial Benefits
  • 401(k) Program + Company Match
  • Profit Sharing Program (via 401(k))
  • Holiday Bonus
  • Performance Incentive Bonus Program
  • Tuition Reimbursement
  • 529 College-Savings Plan
  • Company-Paid Basic Life & AD&D Insurance
• Time Off & Flexibility
  • Paid Vacation
  • Paid Sick Leave
  • 15 Paid Company Holidays
  • 2 Floating Holidays
  • Birthday Off