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Bioinformatics Analyst Jobs in Oregon (NOW HIRING)

Lead the analysis of large-scale cancer and germline multi-omics datasets to extract meaningful insights. Utilize and augment traditional bioinformatics tools with AI-driven techniques to interpret ...

Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods. * Curate, annotate, and validate chemical and ...

Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods. * Curate, annotate, and validate chemical and ...

Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods. * Curate, annotate, and validate chemical and ...

Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods. * Curate, annotate, and validate chemical and ...

Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods. * Curate, annotate, and validate chemical and ...

Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods. * Curate, annotate, and validate chemical and ...

Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods. * Curate, annotate, and validate chemical and ...

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Bioinformatics Analyst information

See Oregon salary details

$7

$48

$87

How much do bioinformatics analyst jobs pay per hour?

As of Aug 29, 2026, the average hourly pay for bioinformatics analyst in Oregon is $48.45, according to ZipRecruiter salary data. Most workers in this role earn between $38.12 and $51.83 per hour, depending on experience, location, and employer.

What does a bioinformatics analyst do?

A bioinformatics analyst works with large databases of omics data, such as genomics studies like the Human Genome Project. Your responsibilities in this career include research on the pathology of diseases and the development of experiments and algorithms to find cures. Your duties also involve ensuring compliance with all federal regulations and protocols. You may document your findings and present them at conferences as well. A career as a bioinformatics analyst requires advanced writing skills for writing scientific literature.

What does a bioinformatics analyst do?

A Bioinformatics Analyst uses computational and statistical methods to analyze biological data, such as DNA, RNA, or protein sequences. They interpret large datasets generated by experiments, develop algorithms, and create visualizations to help researchers understand complex biological processes. Their work supports scientific discoveries in fields like genomics, medicine, and agriculture, often collaborating with biologists, computer scientists, and other researchers.

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

To thrive as a Bioinformatics Analyst, you need a strong background in biology, statistics, and computer science, typically supported by a relevant bachelor's or master's degree. Familiarity with bioinformatics tools like BLAST, Python/R programming, and experience with databases such as GenBank or Ensembl are commonly required. Analytical thinking, problem-solving, and effective communication are crucial soft skills for interpreting data and collaborating with multidisciplinary teams. These skills enable analysts to extract meaningful insights from complex biological data, driving research and innovation in genomics and healthcare.

What are some common challenges faced by bioinformatics analysts when working with large genomic datasets?

One of the main challenges Bioinformatics Analysts encounter is managing and processing extremely large and complex genomic datasets, which often require advanced computational resources and efficient data management strategies. Ensuring data quality and accuracy while integrating information from various sources can also be demanding. Analysts frequently collaborate with biologists, clinicians, and IT professionals to interpret results and optimize workflows, which requires strong communication and interdisciplinary skills.

What is the difference between Bioinformatics Analyst vs Bioinformatics Technician?

AspectBioinformatics AnalystBioinformatics Technician
Required CredentialsBachelor's or Master's in Bioinformatics, Biology, or related field; experience with data analysis toolsAssociate's or Bachelor's; focus on data processing and laboratory support
Work EnvironmentResearch labs, biotech companies, healthcare institutionsLaboratories, research facilities, academic settings
Employer & Industry UsageUsed in research, healthcare, biotech industries for data interpretationUsed for data collection, sample processing, and technical support roles

The main difference is that Bioinformatics Analysts focus on analyzing complex biological data and interpreting results, often requiring advanced degrees. Bioinformatics Technicians typically handle data collection, sample preparation, and technical tasks, supporting analysts and researchers. Both roles are essential in the biotech and healthcare industries, but they differ in responsibilities and required qualifications.

What cities in Oregon are hiring for Bioinformatics Analyst jobs?

Cities in Oregon with the most Bioinformatics Analyst job openings:

Infographic showing various Bioinformatics Analyst job openings in Oregon as of August 2026, with employment types broken down into 85% Full Time, 10% Part Time, and 5% Contract. Highlights an 79% Physical, 8% Hybrid, and 13% Remote job distribution, with an average salary of $100,768 per year, or $48.4 per hour.

Associate Director of Bioinformatics (Women's Health and Organ Health)

OR • On-site, Remote


Natera
Biotechnology Research and Development • 1 - 5K employees

7.7

Company rating: 7.7 out of 10

Based on 38 frontline employees who took The Breakroom Quiz

56th of 120 rated laboratories

People enjoy working here

Good employer

Paid breaks


Full-time

Posted 18 days ago


Job description

Natera is seeking an Associate Director to lead a team of Bioinformatics Scientists in our Women's Health and Organ Health research organization. You will oversee the scientific planning and execution of research and assay-development of diagnostic projects, and you will be responsible for creating production-ready pipeline components. You will manage your team and its professional growth, the research strategy behind new product development, and the handoff of research work into production. 

The role calls for deep experience in algorithm and assay development, NGS data processing across multiple modalities, and the full life cycle of diagnostic product research and development in a regulated (CLIA) setting. You will evaluate new technologies and NGS assays, implement methods to optimize performance, and help define the product profile from a bioinformatics perspective, with input from R&D, Product, and Laboratory Directors.

Primary Responsibilities:

Team Leadership and Development: Lead and mentor a team of Bioinformatics Scientists. Own their professional growth, their responsibilities, and the standard the team holds itself to.

Research Strategy: Own the research strategy behind new product development, from scoping through execution, working with cross-functional stakeholders on what the team takes on and in what order.

Assay Science: Lead bioinformatics analysis for assay development and optimization, and troubleshoot experiments alongside laboratory scientists. This spans multi-omic approaches including methylation and fragmentomics and other cell-free DNA (cfDNA) derived features. It also spans short-read and long-read sequencing and both hybrid-capture and amplicon target enrichment.

Study Design and Data Quality: Partner with laboratory teams on study design at the research and feasibility stage, covering new technology assessment, optimization, and performance determination. Make sure the resulting data holds up before anyone builds on it.

Analysis Method Improvement: Advise and prototype improvements to analysis pipelines, including variant detection, quality control, and modality-specific processing: methylation calling, fragment-size and end-motif analysis, error suppression for deep targeted panels, and structural-variant calling.

Production Readiness and Handoff: Move research prototypes into stable production workflows. Hold the team to software engineering practice, including version control, testing, continuous integration and delivery, and containerization. Automate the routine parts of research data management, pipeline execution, and reporting so the team's time goes to science.

Ways of Working: AI is a routine part of the work here. As the leader of this team you set what correct use looks like: what an agent may conclude on its own, and what requires a scientist to sign off. We do not screen for prior experience with these tools, and many strong candidates come from environments where they were restricted; we provide the tooling and the ramp time.

Cross-functional Partnership: Work with data science, molecular biology, pipeline engineering, biostatistics, quality assurance, and laboratory operations on new products and on the transition of research work into production. Act as the subject-matter expert those groups come to, and explain findings and the roadmap clearly at every level of the company.

What success looks like after a year:
  • The team has a research roadmap you own, and progress against it is visible outside the team.
  • You independently advise and guide research strategies in cross functional settings.
  • New analysis methods from your team are implemented within production code and demonstrate expected performance on benchmark studies..
  • You and team members become trusted experts that other functions direct their hard questions to, and can independently support decision making.
  • AI agent-assisted work is normal on the team, with a standard for it that you set.
Qualifications:
  • Ph.D. in Bioinformatics, Computational Biology, Computer Science, Mathematics, Engineering, Biostatistics, or a related field. (An M.S. with equivalent experience considered).
  • 7+ years in bioinformatics, including 3+ years leading scientists and working across functions.
  • Demonstrated ownership of a team that took scientific research into real clinical use, not only to a result.
  • Demonstrated experience developing or scientifically guiding diagnostic sequencing assays.
Knowledge, Skills, and Abilities:What we are screening for
  • Deep expertise in next-generation sequencing analysis: hybrid-capture or amplicon-based target enrichment designs, sequencing quality control, secondary analysis, and variant calling.
  • Experience with cell-free DNA sequencing and other omics data, especially in a diagnostics setting. Preferred to have some exposure to multiomics types : methylation, fragmentomics, or related cfDNA signals.
  • Working  knowledge of how a diagnostic product moves through development in a regulated, accredited setting, including the practices and standards that apply.
  • Strong Python, programming, and data analysis skills.
  • Fluency in exploratory data analysis and visualization on complex data sets, and the ability to translate findings into actionable recommendations.
  • Understanding of sequencing workflows from sample extraction through the instrument, deep enough to tell a biology problem from an analysis problem.
  • A demonstrated record of assessing a new platform, assay chemistry or method against established benchmarks..
  • Clear communication of technical detail to people who do not share your background.
  • The ability to run several objectives and timelines at once without close supervision.
Strong candidates may also have
  • Long-read sequencing (PacBio HiFi, Oxford Nanopore) in addition to short-read (Illumina).
  • R, shell scripting, or Java.
  • Cloud computing (AWS, GCP) and workflow orchestration (Snakemake, WDL, Nextflow, Cromwell).
  • Containerization (Docker, Kubernetes) for reproducible, production-grade analyses.
  • A record of conference presentations or peer-reviewed publication.
  • A desire to work in a fast-paced environment where a small team has high impact.


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