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Bioinformatic Engineer Jobs in Colorado (NOW HIRING)

Data Analyst

Boulder, CO ยท On-site

$100K - $150K/yr

Bachelor's or Master's in Statistics, Biostatistics, Chemical Engineering, Bioinformatics, Cheminformatics, or related fields with 5-8 years of experience, or a PhD with 3-5 years of experience.

Experience developing bioinformatics workflows for NGS assays for non-technical users Travel, Motor ... Python programming, machine learning methods, and NextFlow * developing gene expression phenotype ...

Staff Scientist

Boulder, CO ยท On-site

$120K - $140K/yr

Experience developing bioinformatics workflows for NGS assays for non-technical users Travel, Motor ... Python programming, machine learning methods, and NextFlow * developing gene expression phenotype ...

Showing results 41-52

Bioinformatic Engineer information

See Colorado salary details

$34.7K

$93.8K

$149.3K

How much do bioinformatic engineer jobs pay per year?

As of Sep 15, 2026, the average yearly pay for bioinformatic engineer in Colorado is $93,778.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,900.00 and $114,600.00 per year, depending on experience, location, and employer.

What is a bioinformatic engineer?

Bioinformatic Engineers are professionals who develop and apply computational tools and techniques to analyze biological data, such as DNA sequences, protein structures, and genetic information. They combine expertise in computer science, biology, and mathematics to process large datasets generated by modern biological experiments. Their work supports research in areas like genomics, drug discovery, and personalized medicine by helping scientists make sense of complex biological data.

What skills and qualifications are needed to thrive as a bioinformatic engineer?

To thrive as a Bioinformatic Engineer, you need a strong background in computational biology, programming (such as Python or R), statistics, and a relevant degree in bioinformatics, computer science, or a related field. Familiarity with bioinformatics tools (like BLAST, GATK, or Bioconductor), data analysis platforms, and experience with databases such as SQL are typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help you interpret complex data and collaborate with multidisciplinary teams. These skills are essential for accurately analyzing biological data, deriving meaningful insights, and facilitating research or clinical advancements.

What are common challenges faced by bioinformatic engineers when working with large-scale genomic data?

Bioinformatic Engineers often encounter challenges related to managing and processing vast amounts of genomic data, which can require significant computational resources and efficient data handling strategies. Ensuring data integrity, reproducibility of analyses, and effective collaboration with multidisciplinary teams of biologists, statisticians, and software developers are also key aspects of the role. Staying updated with the latest bioinformatics tools and pipelines is essential, as the field evolves rapidly. Overcoming these challenges requires strong problem-solving skills, attention to detail, and the ability to communicate complex findings to non-technical stakeholders.

What is the difference between Bioinformatic Engineer vs Bioinformatics Analyst?

AspectBioinformatic EngineerBioinformatics Analyst
Required CredentialsBachelor's or Master's in Bioinformatics, Computer Science, or related fieldsBachelor's or Master's in Bioinformatics, Biology, or related fields
Work EnvironmentResearch labs, biotech companies, healthcare institutionsResearch institutions, healthcare, pharmaceutical companies
Employer & Industry UsageDevelops tools, pipelines, and software for biological data analysisAnalyzes biological data, interprets results, and reports findings

While both roles involve biological data, Bioinformatic Engineers focus on developing computational tools and pipelines, whereas Bioinformatics Analysts primarily interpret data and generate insights. Both positions require similar educational backgrounds and are vital in research and healthcare settings, but their core responsibilities differ in development versus analysis.

What are popular job titles related to Bioinformatic Engineer jobs in Colorado?

For Bioinformatic Engineer jobs in Colorado, the most frequently searched job titles are:

What job categories do people searching Bioinformatic Engineer jobs in Colorado look for?

The top searched job categories for Bioinformatic Engineer jobs in Colorado are:

Infographic showing various Bioinformatic Engineer job openings in Colorado as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $93,778 per year, or $45.1 per hour.

Biostatistics Scientist (Plant Science)

Woodland Park, CO โ€ข On-site

$90K - $105K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 13 days ago


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, markerโ€‘assisted selection, genomic prediction, and dataโ€‘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 fieldโ€‘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 0โ€“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, fieldโ€‘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 lifeโ€‘science data analysis.
  • Experience in genomic prediction, QTL mapping, GWAS, markerโ€‘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 crossโ€‘functional audiences.
  • Exposure to cloudโ€‘based, Linux, Git, or highโ€‘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 nonโ€‘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 crossโ€‘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 Collegeโ€‘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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