Biostatistics Scientist
Listed on 2026-07-31
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Research/Development
Genetics / Genomics, Data Scientist, Research Scientist, Biotech Research
Job Summary
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.
Job SummaryThe 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.
Key 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.
- 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…
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