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Cancer Bioinformatics Jobs (NOW HIRING)

Natera

Senior Bioinformatics Scientist

OR · On-site +1

These will be the models that detect cancer in the minimal residual disease (MRD) setting and help ... D. in Bioinformatics, Computer Science, Engineering, Biochemistry, or a related field, with a ...

Axle

Bioinformatics Scientist

Bethesda, MD · On-site

Bioinformatics Scientist To be considered for this position, please upload a document as your Cover ... cancer research, etc.) * Provide examples of basic machine learning/AI concepts and/or how you've ...

Axle

Bioinformatics Scientist

Bethesda, MD · On-site

Bioinformatics Scientist To be considered for this position, please upload a document as your Cover ... cancer research, etc.) * Provide examples of basic machine learning/AI concepts and/or how you've ...

Axle

Bioinformatics Scientist

Bethesda, MD

Bioinformatics Scientist To be considered for this position, please upload a document as your Cover ... cancer research, etc.) * Provide examples of basic machine learning/AI concepts and/or how you've ...

Arc Bio

Bioinformatics Scientist

Scotts Valley, CA · On-site

... cancer genomics. Prior experience with proximity ligation (Hi-C) is not required, but it is ... Contribute and author bioinformatics-focused white papers, posters, and publications.

NYULMC

Senior Bioinformatics Programmer

New York, NY · On-site

In addition, bioinformatics analysis of various types of patient genomic, epigenomic and ... based cancer diagnostics, clinical genomics and hands on precision medicine for the benefit of ...

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How much do cancer bioinformatics jobs pay per year?

As of Sep 10, 2026, the average yearly pay for cancer bioinformatics in the United States is $203,468.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,500.00 and $400,000.00 per year, depending on experience, location, and employer.

What is cancer bioinformatics?

A Cancer Bioinformatics job involves analyzing and interpreting large-scale biological data to understand cancer biology and improve treatment strategies. Professionals in this field use computational tools, machine learning, and statistical methods to study genomic, transcriptomic, and clinical data. They work closely with oncologists, biologists, and data scientists to identify biomarkers, track tumor evolution, and develop personalized medicine approaches. This role is essential for advancing cancer research, optimizing therapies, and contributing to precision oncology.

What are the typical daily responsibilities of a cancer bioinformatics specialist?

Professionals in Cancer Bioinformatics spend their days analyzing large-scale genomic datasets from cancer patients, designing algorithms to identify genetic mutations, and interpreting the biological significance of their findings. They often collaborate closely with oncologists, laboratory scientists, and other bioinformaticians to translate data into clinically meaningful insights. Tasks may also include developing and maintaining computational pipelines, preparing reports or visualizations, and staying updated on new analytical methods. This role blends technical and analytical work with teamwork, offering a dynamic work environment where your contributions can directly impact cancer research and patient outcomes.

What are the key skills and qualifications needed for cancer bioinformatics?

To excel in Cancer Bioinformatics, you typically need a strong background in bioinformatics, computational biology, statistical analysis, and a degree in a life science or computational field. Familiarity with tools such as R, Python, next-generation sequencing (NGS) platforms, and commonly used databases like TCGA or COSMIC is vital, and certifications in bioinformatics or genomics can be advantageous. Excellent problem-solving abilities, teamwork, and strong communication skills help in collaborating effectively with multidisciplinary researchers and clinicians. Mastering these skills ensures accurate analysis of complex cancer datasets, supports collaborative discoveries, and advances precision oncology research.

What is the role of bioinformatics in cancer?

In cancer bioinformatics, professionals analyze large-scale genomic and molecular data to identify genetic mutations, biomarkers, and pathways involved in cancer development and progression. They use computational tools and algorithms to interpret sequencing data, aiding in diagnosis, personalized treatment, and research efforts.
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Cities with the most Cancer Bioinformatics job openings:

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Cancer Bioinformatics jobs near you
Infographic showing various Cancer Bioinformatics job openings in the United States as of September 2026, with employment types broken down into 3% Internship, 3% As Needed, 82% Full Time, 10% Part Time, and 2% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $203,468 per year, or $97.8 per hour.

Senior Bioinformatics Scientist

OR • On-site, Remote

Natera
Biotechnology Research and Development • 1 - 5K employees
Learn more about Nateraexternal

Full-time

Re-posted 10 days ago

Natera rating

7.8

Company rating: 7.8 out of 10

Based on 39 frontline employees who took The Breakroom Quiz

Job description

Natera is seeking a Senior Bioinformatics Scientist to join our Bioinformatics Research team and help build the machine learning models behind Natera's tissue-free, methylation-based assay. These will be the models that detect cancer in the minimal residual disease (MRD) setting and help inform treatment selection. The ideal candidate brings a strong background in algorithm development, genomics, sequencing data processing, and applied machine learning.

Primary Responsibilities:

  • Develop, train, and evaluate machine learning models, driving algorithm design decisions that support cancer detection in the MRD setting.
  • Own end-to-end research analyses, from ideation through implementation (scripts and notebooks), troubleshooting, and performance evaluation, including developing new features within existing pipelines.
  • Translate between wet-lab experimental design and computational analysis, navigating ambiguity as assay requirements evolve and maintaining rigorous quality control across high-volume sequencing data spanning multiple cohorts, vendors, and clinical protocols.
  • Communicate findings and model performance to both technical and cross-functional stakeholders, and contribute to establishing standards for code quality and reproducibility.

Qualifications

  • Ph.D. in Bioinformatics, Computer Science, Engineering, Biochemistry, or a related field, with a strong focus on cancer epi/genomics with 0-3 years of professional experience.
  • Master in Bioinformatics, Computer Science, Engineering, Biochemistry, or a related field, with a strong focus on cancer epi/genomics with 4-6 years of professional experience.

Knowledge, Skills, and Abilities:

  • Deep theoretical and practical understanding of high-throughput DNA sequence data analysis, including mapping, sequence alignment, and variant calling workflows.
  • Experience in algorithm development and data analysis, including applying and evaluating statistical methods.
  • Demonstrated experience in developing core ML models, including generalized linear models, kernel methods, tree-based algorithms, and neural networks, with a focus on biological data (e.g., DNA sequencing data)
  • Strong quantitative reasoning and data analysis skills, with a demonstrated ability to apply them effectively to relevant scientific problems.
  • Experience with Python and Linux command-line tools, including writing shell scripts. Strong computational and programming skills, including thorough experience with Python statistical packages (NumPy, Matplotlib, Pandas).
  • Familiarity with public databases such as TCGA, COSMIC, or OncoKB.
  • Ability to visualize findings and summarize results in oral and written form for both technical and non-technical audiences.
  • High scientific rigor, with eagerness to teach and learn new computational methods and biology.
  • Ability to work on a cross-functional team in a highly collaborative environment, partnering with both computational and experimental scientists.

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