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Genomics Data Scientist Jobs in Colorado (NOW HIRING)

... Genomics is a global life science company based in Boulder, Colorado with an R&D and Production ... Oversee regular backups of data, systems, and configurations, ensuring that backups are complete ...

... Genomics is a global life science company based in Boulder, Colorado with an R&D and Production ... Oversee regular backups of data, systems, and configurations, ensuring that backups are complete ...

Staff Scientist

Boulder, CO · On-site

$120K - $140K/yr

... genomics breakthroughs across fields like cancer, infectious disease, rare genetic disorders, and ... NGS data analysis * Foundational knowledge of human genetics, cancer biology, and statistics

Staff Scientist

Boulder, CO · On-site

$120K - $140K/yr

... genomics breakthroughs across fields like cancer, infectious disease, rare genetic disorders, and ... NGS data analysis * Foundational knowledge of human genetics, cancer biology, and statistics

... genomics breakthroughs across fields like cancer, infectious disease, rare genetic disorders, and ... Advance AI enablement and data science capabilities by identifying and scaling smart manufacturing ...

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Genomics Data Scientist information

See Colorado salary details

$39.4K

$129.1K

$206.6K

How much do genomics data scientist jobs pay per year?

As of Sep 3, 2026, the average yearly pay for genomics data scientist in Colorado is $129,062.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,600.00 and $143,000.00 per year, depending on experience, location, and employer.

What is a genomics data scientist?

A Genomics Data Scientist analyzes large-scale genomic data to extract meaningful biological insights. They use statistical models, machine learning, and bioinformatics tools to study genetic variations, disease associations, and evolutionary patterns. Their work supports medical research, drug discovery, and precision medicine by translating complex genetic data into actionable knowledge. This role often involves coding in languages like Python or R, working with databases, and collaborating with biologists and clinicians.

What are the key skills and qualifications needed to thrive as a genomics data scientist?

To thrive as a Genomics Data Scientist, you need a solid background in biology, bioinformatics, statistics, and programming—typically with an advanced degree in a relevant scientific field. Proficiency with tools such as Python, R, next-generation sequencing (NGS) analysis pipelines, and experience with cloud-based data platforms and genomics databases is highly valued, and certifications in data science or bioinformatics can be advantageous. Critical thinking, strong communication, and interdisciplinary teamwork skills distinguish those who excel in this collaborative and evolving field. These core and soft skills enable accurate analysis of complex genomic data, effective collaboration with scientific and technical teams, and meaningful contributions to biomedical research and healthcare innovation.

What are some common challenges that genomics data scientists face in their daily work?

Genomics Data Scientists commonly face challenges such as handling massive and complex sequencing datasets, integrating information from various sources, and ensuring data quality and reproducibility. They often need to stay current with rapidly evolving bioinformatics tools and computational methods to interpret new types of genomic data. Collaborating across interdisciplinary teams—including biologists, clinicians, and software engineers—can require excellent communication and translation of technical insights into actionable results. Managing these challenges is rewarding, as it contributes directly to discoveries in medical research, diagnostics, and personalized medicine.

What are the most commonly searched types of Genomics Data Scientist jobs in Colorado?

The most popular types of Genomics Data Scientist jobs in Colorado are:

What job categories do people searching Genomics Data Scientist jobs in Colorado look for?

The top searched job categories for Genomics Data Scientist jobs in Colorado are:

What cities in Colorado are hiring for Genomics Data Scientist jobs?

Cities in Colorado with the most Genomics Data Scientist job openings:

Infographic showing various Genomics Data Scientist job openings in Colorado as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, 1% Temporary, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $129,062 per year, or $62 per hour.

Biostatistics Scientist (Plant Science)

Sakata Seed America, INC.

Woodland Park, CO • On-site

$90 - $105/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 2 days ago

New


Job description

If you are unable to complete this application due to a disability, contact this employer to ask for an accommodation or an alternative application process.

Full Time Professional Woodland, CA, US

Salary Range: $90,000.00 To $105,000.00 Annually

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.

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.
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
  • 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.
  • 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
  • Reports to Senior Biotech Manager

Works under the guidance of senior scientists, project leads, and cross‑functional research teams

BENEFITS
  • Medical, Dental & Vision Insurance
  • Monthly Wellness Stipend
  • Employee Assistance Program (EAP)
  • Employee Philanthropic Giving Program
  • Disability Insurance (plans vary by location)
  • 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
  • Paid Vacation
  • Paid Sick Leave
  • 15 Paid Company Holidays
  • 2 Floating Holidays
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