Biostatistics Scientist (Plant Science

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Full Time Professional Woodland, CA, US

7 days ago Requisition

Salary Range: $90,000.00 To $ Annually

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.

• 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.
• 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.

• 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.

• 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.

• 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; industry experience a plus

• 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.

• 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…