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Computational Biologist Jobs (NOW HIRING)

Biohub

Computational Biologist

Chicago, IL · On-site

$130K - $163K/yr

The Opportunity The Biohub Chicago is seeking an exceptional Computational Biologist, Spatiotemporal Multi-Omics to join our growing team. We are looking for a creative and collaborative ...

Biohub

Computational Biologist

Chicago, IL

$130K - $163K/yr

The Opportunity The Biohub Chicago is seeking an exceptional Computational Biologist, Spatiotemporal Multi-Omics to join our growing team. We are looking for a creative and collaborative ...

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How much do computational biologist jobs pay per year?

As of Sep 2, 2026, the average yearly pay for computational biologist in the United States is $93,988.00, according to ZipRecruiter salary data. Most workers in this role earn between $70,500.00 and $117,000.00 per year, depending on experience, location, and employer.

What is a computational biologist?

A computational biologist is a skilled scientist who uses complex computer algorithms to research and analyze biological systems. This highly specialized job entails using computers and advanced data analytics software to research biological topics such as genetic sequencing, cellular growth numbers, and protein sampling. As a computational biologist, your duties are to code computer algorithms and perform bioinformatics research in the lab. You may also work with students by using the data from their bioinformatics research.

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

To thrive as a Computational Biologist, you need a strong background in biology, mathematics, statistics, and computer science, often supported by an advanced degree in a relevant field. Proficiency with programming languages (such as Python or R), bioinformatics tools, and data analysis platforms is essential. Strong problem-solving abilities, attention to detail, and effective communication are standout soft skills for this role. These skills are critical for analyzing complex biological data, interpreting results, and collaborating with multidisciplinary teams to advance scientific research.

What are some common interdisciplinary collaborations for a computational biologist, and how do these impact daily work?

Computational Biologists frequently collaborate with laboratory scientists, statisticians, and software engineers to analyze complex biological data. These interdisciplinary interactions mean that communication skills are essential, as you’ll often translate computational findings into actionable insights for experimental teams. Daily responsibilities may include attending joint meetings, discussing data analysis strategies, and integrating feedback from collaborators to refine models. This collaborative environment fosters both scientific discovery and personal growth, offering exposure to diverse perspectives and expertise.
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Computational Biologist jobs near you
Infographic showing various Computational Biologist job openings in the United States as of August 2026, with employment types broken down into 71% Full Time, 26% Part Time, 1% Temporary, and 2% Contract. Highlights an 69% Physical, 2% Hybrid, and 29% Remote job distribution, with an average salary of $93,988 per year, or $45.2 per hour.

Junior Computational Biologist (Remote)

Astrix Inc

South San Francisco, CA • On-site, Remote

$30 - $34/hr

Full-time

Re-posted 21 days ago

Job description

Pay Rate Low: 30 | Pay Rate High: 34
A leading biotechnology research organization is seeking a Junior Computational Biologist to support efforts in refining how cellular states are quantified and validated!
Title: Jr. Computational Biologist (Remote Contract)
Location: Remote (Must be available during PST business hours)
Compensation: $30-34/hour + benefits
Contract Duration: 6-12+ months
Job Duties:
This project will focus on benchmarking functional scoring methodologies and improving interpretability of high-dimensional transcriptomic datasets.
The selected candidate will contribute to distinguishing true biological signal from technical variation in large-scale single-cell atlases, directly enhancing the reliability of automated cell-state classification frameworks.
Start Date: July 1, 2026
  • Duration: Through December 18, 2026
  • Commitment: Full-time (100%)
  • Ideal Candidate: Upcoming June 2026 PhD graduate or recent PhD graduate
  • Location: Onsite in South San Francisco, CA preferred; remote within the U.S. considered (must work PST hours)
  • Visa Sponsorship: Not availabl

Key Responsibilities
  • Systematically evaluate and benchmark computational approaches for quantifying phenotype activation across single-cell transcriptomic datasets.
  • Establish rigorous statistical baselines and negative-control frameworks to improve the robustness of automated cell-state classification methods.
  • Develop or refine computational methods to address limitations in current approaches.
  • Design strategies to distinguish genuine biological signatures from stochastic or technical noise.
  • Present findings in internal scientific reviews and contribute to potential conference abstracts or peer-reviewed publications.

Required Qualifications
  • Extensive hands-on experience in single-cell data analysis using Scanpy, AnnData, and Pandas.
  • Strong proficiency implementing statistical and machine learning models using scikit-learn and SciPy.
  • Demonstrated commitment to reproducible research practices and well-organized code.
  • Ability to clearly communicate complex computational concepts to interdisciplinary scientific teams.
  • Master's degree with ongoing PhD pursuit, or recent PhD graduate, in Computational Biology, Computer Science, Machine Learning, or related quantitative discipline.
  • Interest in drug discovery and comfort working in dynamic, research-driven environments.

Preferred Qualifications
  • Background knowledge in cell biology and/or immunology.
  • Experience with hypothesis testing, noise modeling, and benchmarking computational tools.
  • Familiarity with Explainable AI (XAI) approaches or large-scale biological datasets.
  • Demonstrated ability to build or extend novel bioinformatics pipelines.
    INDBH
    #LI-MG1

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