Biostatistics Scientist (Plant Science)

Sakata Seed America, Inc.

Woodland (CA)

On-site

USD 90,000 - 120,000

Full time

14 days+

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401(k)

Job summary

The Biostatistics Scientist at Sakata Seed America, Inc. 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 guidance from senior scientists and cross-functional teams. The role emphasizes developing reproducible analytical workflows to improve trait evaluation, marker-assisted selection, genomic prediction, and data-driven breeding

Qualifications

PhD/MS in Biostatistics, Quantitative Genetics, plant breeding, computational biology, data science, or related field.
Experience with statistical analysis of biological/genomic data preferred.

Responsibilities

Develop and apply statistical models for vegetable crop breeding programs.
Analyze genomic, phenotypic, and field-trial data with reproducible workflows.
Collaborate with breeders, molecular scientists, and bioinformatics teams.

Skills

Biostatistics

Quantitative Genetics

Genomic Prediction

R/Python

Education

PhD in Biostatistics/Quantitative Genetics/Plant Breeding

MS in Biostatistics/Related Field with 0–2 years experience

Tools

Python

SAS

Job description

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 Summary

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.

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.

Required Qualifications

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; industry experience a plus

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.

Reporting Structure