ZipRecruiter

Log In

2

Entry Level Computational Biology Jobs (NOW HIRING)

University of Utah

Post Doc Res Assoc

Campus, IL

$65K - $73K/yr

  • Retirement

... P1 - Entry Level Pro FLSA Code Administrative Patient Sensitive Job Code? No Standard Hours per ... Computational biology / bioinformatics, especially ribosome profiling, disease gene discovery, or ...

University of Utah

Post Doc Res Assoc

Campus, IL · On-site

$65K - $73K/yr

  • Retirement

... P1 - Entry Level Pro FLSA Code Administrative Patient Sensitive Job Code? No Standard Hours per ... Computational biology / bioinformatics, especially ribosome profiling, disease gene discovery, or ...

University of Utah

Post Doc Res Assoc

Campus, IL

$65K - $73K/yr

  • Retirement

... P1 - Entry Level Pro FLSA Code Administrative Patient Sensitive Job Code? No Standard Hours per ... Computational biology / bioinformatics, especially ribosome profiling, disease gene discovery, or ...

University of Utah

Post Doc Res Assoc

Salt Lake City, UT · On-site

$65K - $73K/yr

  • Retirement

... P1 - Entry Level Pro FLSA Code Administrative Patient Sensitive Job Code? No Standard Hours per ... Computational biology / bioinformatics, especially ribosome profiling, disease gene discovery, or ...

University of Utah

Post Doc Res Assoc

Salt Lake City, UT · On-site

$65K - $73K/yr

  • Retirement

... P1 - Entry Level Pro FLSA Code Administrative Patient Sensitive Job Code? No Standard Hours per ... Computational biology / bioinformatics, especially ribosome profiling, disease gene discovery, or ...

next page

Showing results 1-20

All JobsComputational Biology JobsEntry Level Computational Biology Jobs

Entry Level Computational Biology information

See salary details

$33.5K

$48.3K

$63.5K

How much do entry level computational biology jobs pay per year?

As of Aug 14, 2026, the average yearly pay for entry level computational biology in the United States is $48,298.00, according to ZipRecruiter salary data. Most workers in this role earn between $38,000.00 and $55,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an entry level computational biologist, and why are they important?

To thrive as an Entry Level Computational Biologist, you generally need a strong background in biology, statistics, and computer science, often supported by a relevant bachelor's or master's degree. Familiarity with programming languages such as Python or R, bioinformatics tools (e.g., BLAST, Bioconductor), and experience using databases like GenBank are typically required. Strong problem-solving ability, attention to detail, and effective communication skills help individuals interpret complex data and collaborate with interdisciplinary teams. These skills are vital for accurately analyzing biological data and contributing meaningful insights to research projects.

What is an entry level computational biologist?

Entry level computational biologists are professionals who use computational techniques, such as programming, data analysis, and mathematical modeling, to study biological systems and solve problems in areas like genomics, proteomics, and drug discovery. They typically work in research labs, universities, biotech companies, or healthcare organizations. Entry level roles are designed for recent graduates or those new to the field, often requiring a bachelor’s or master’s degree in computational biology, bioinformatics, computer science, or a related discipline. These positions offer opportunities to learn and grow while working alongside experienced scientists on interdisciplinary projects.

What are the typical projects or tasks an entry level computational biologist might work on in their first year?

As an entry level computational biologist, you can expect to work on tasks such as analyzing biological datasets, developing or optimizing scripts for data processing, and assisting with the interpretation of genomic or proteomic data. You'll often collaborate with wet-lab biologists to translate their experimental results into computational models or visualizations. Additionally, you may help maintain databases, document workflows, and participate in regular team meetings to discuss progress and troubleshoot challenges. These foundational experiences will help you build both technical expertise and communication skills essential for advancement in the field.

What is the difference between Entry Level Computational Biology vs Data Analyst?

AspectEntry Level Computational BiologyData Analyst
Required CredentialsBachelor's in Biology, Bioinformatics, or related field; basic programming skillsBachelor's in Statistics, Mathematics, or related field; proficiency in data tools
Work EnvironmentResearch labs, biotech companies, academic institutionsBusiness, healthcare, finance sectors, often office-based
Employer & Industry UsageBiotech firms, pharmaceutical companies, research institutionsCorporations, healthcare providers, consulting firms
Common Search & ComparisonYesYes

Entry Level Computational Biology focuses on applying computational techniques to biological data, often in research or biotech settings. Data Analysts interpret data across various industries, including healthcare and finance. While both roles require analytical skills, computational biology emphasizes biological knowledge and bioinformatics tools, whereas data analysis centers on statistical and data visualization skills. The roles overlap in data handling but differ in domain expertise and work environment.

What are entry-level jobs in computational biology?

Entry-level jobs in computational biology focus on using computers to help analyze genomic data and bioinformatics. In layman's terms, this means using electronic devices to help research treatments, therapies, and cures for various diseases. As an entry-level employee, you may work under the supervision of senior computational biologists, process experimental data, and help define the parameters for data modeling. This job is something of a mix between biologist, software developer, and statistician, with a particular emphasis on understanding the underlying methods and information used in computational biology. This is a complex field, so many employers use this entry-level job as a form of extended training to ensure you have the skills you need for working in this industry.

More about Entry Level Computational Biology jobs

What cities are hiring for Entry Level Computational Biology jobs?

Cities with the most Entry Level Computational Biology job openings:

What are the most commonly searched types of Computational Biology jobs?

The most popular types of Computational Biology jobs are:

What states have the most Entry Level Computational Biology jobs?

States with the most job openings for Entry Level Computational Biology jobs include:

What job categories do people searching Entry Level Computational Biology jobs look for?

The top searched job categories for Entry Level Computational Biology jobs are:

Entry Level Computational Biology jobs near you
Infographic showing various Entry Level Computational Biology job openings in the United States as of August 2026, with employment types broken down into 63% Full Time, 32% Part Time, 1% Temporary, and 4% Contract. Highlights an 80% Physical, 2% Hybrid, and 18% Remote job distribution, with an average salary of $48,298 per year, or $23.2 per hour.

Biostatistics Scientist (Plant Science)

Sakata Seed America, INC.

Woodland, CA • On-site

$90K - $105K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 17 days ago

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.

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

  • Reports to Senior Biotech Manager

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

BENEFITS:

Health & Wellness
Medical, Dental & Vision Insurance
Monthly Wellness Stipend
Employee Assistance Program (EAP)

Employee Philanthropic Giving Program

Disability Insurance (plans vary by location)


Financial Benefits
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


Time Off & Flexibility
Paid Vacation
Paid Sick Leave
15 Paid Company Holidays
2 Floating Holidays

Birthday Off