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How much do bioinformatics scientist jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for bioinformatics scientist in Portland, OR is $58.11, according to ZipRecruiter salary data. Most workers in this role earn between $47.69 and $66.30 per hour, depending on experience, location, and employer.

What is a bioinformatics scientist?

A Bioinformatics Scientist is a professional who uses computational tools and techniques to analyze and interpret biological data, such as DNA, RNA, and protein sequences. They combine expertise in biology, computer science, mathematics, and statistics to solve complex problems in genomics, drug discovery, and other life sciences fields. Bioinformatics Scientists often work with large datasets, develop algorithms, and create databases to help researchers understand biological processes and make scientific discoveries.

How do bioinformatics scientists typically collaborate with biologists and other researchers on multidisciplinary projects?

Bioinformatics Scientists often work closely with biologists, chemists, and data analysts to interpret complex biological data. Collaboration usually involves translating biological questions into computational problems, developing or applying algorithms, and communicating findings in a way that is accessible to non-technical team members. Effective teamwork requires strong communication skills and the ability to bridge the gap between experimental data and computational analysis, ensuring that research goals are aligned across disciplines.

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

To thrive as a Bioinformatics Scientist, you need a strong background in biology, statistics, and computer science, typically supported by an advanced degree in bioinformatics or a related field. Proficiency with programming languages (such as Python, R, or Perl), bioinformatics databases, and data analysis platforms like BLAST or Galaxy is essential. Critical thinking, problem-solving, and clear communication are vital soft skills for interpreting complex data and collaborating with multidisciplinary teams. These skills and qualities are crucial for extracting meaningful insights from large biological datasets and advancing scientific research.

What is the difference between Bioinformatics Scientist vs Bioinformatics Analyst?

AspectBioinformatics ScientistBioinformatics Analyst
Required CredentialsTypically requires a Master's or Ph.D. in Bioinformatics, Computational Biology, or related fieldsUsually holds a Bachelor's or Master's degree in Bioinformatics, Biology, or related areas
Work EnvironmentResearch labs, biotech companies, academic institutions, often involved in developing new algorithms and toolsData analysis in clinical or research settings, interpreting existing data sets
Employer & Industry UsageUsed in biotech, pharma, academia for research and developmentCommon in healthcare, clinical research, and biotech for data interpretation

In summary, Bioinformatics Scientists typically hold advanced degrees and focus on developing new computational methods, while Bioinformatics Analysts often have a more applied role, interpreting data to support research or clinical decisions.

Is bioinformatics well paying?

Bioinformatics scientists typically earn competitive salaries due to their specialized skills in computational biology, data analysis, and programming. Salaries vary by experience, location, and industry, but the field generally offers above-average compensation compared to many other scientific roles.

What do you do as a bioinformatics scientist?

A bioinformatics scientist analyzes biological data, such as genetic sequences and molecular structures, using computational tools and algorithms. They develop and apply software to interpret complex biological information, often working with large datasets and programming languages like Python or R in research or healthcare settings.

What are popular job titles related to Bioinformatics Scientist jobs in Portland, OR?

For Bioinformatics Scientist jobs in Portland, OR, the most frequently searched job titles are:

What job categories do people searching Bioinformatics Scientist jobs in Portland, OR look for?

The top searched job categories for Bioinformatics Scientist jobs in Portland, OR are:

What cities near Portland, OR are hiring for Bioinformatics Scientist jobs?

Cities near Portland, OR with the most Bioinformatics Scientist job openings:

Bioinformatics Scientist jobs near you
Infographic showing various Bioinformatics Scientist job openings in Portland, OR as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 93% Full Time, 3% Part Time, and 2% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $120,861 per year, or $58.1 per hour.

Bioinformatics Scientist - Gene Regulation & Cellular Reprogramming

e184

Portland, OR • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 11 days ago

Job description

About us

e184 Repro is a biotechnology research company with the mission of advancing in vitro gametogenesis to solve one of biology's most profound challenges: returning the fundamental right to procreate.

We work at the frontier of cutting-edge technology, integrating cellular reprogramming, machine learning-guided optimization, multi-omics analysis, and automated experimental workflows to enable gamete development for individuals facing reproductive challenges.

Role overview

As a Bioinformatics Scientist with a cellular reprogramming background, you will lead computational analysis of multi-modal genomics data (scRNA-seq, ATAC-seq) to identify transcription factor combinations driving desired cell state conversion. This role focuses on gene regulatory network inference, differential analysis of single-cell transcriptomics, and computational prioritization of TF cocktails for cellular reprogramming, requiring deep expertise in multi-platform scRNA-seq analysis and transcriptional regulation biology. You will collaborate closely with wet lab teams to translate computational predictions into experimental designs, while also exploring hybrid approaches that integrate foundation model insights into our reprogramming pipeline.

What youll do

  • Lead end-to-end TF discovery for cellular reprogramming - from multi-platform single-cell genomics analysis (scRNA-seq, ATAC-seq) through GRN inference, differential analysis, and trajectory mapping - to nominate the regulators that flip cell fate.

  • Crack the combinatorial code of reprogramming by ranking TF cocktails as actionable combinations and decoding pooled perturbation and CRISPRa screens at single-cell resolution.

  • Read regulatory grammar straight off the chromatin - accessibility, motifs, synergy, repression - and build the data backbone that harmonizes modalities and platforms into something we can actually model on.

  • Sit shoulder-to-shoulder with wet lab teammates, closing the loop between predictions and screens: ingest fresh NGS readouts, retrain, re-prioritize, and pick the next experiment that teaches the model the most.

Core requirements

  • PhD in Bioinformatics, Computational Biology, or related quantitative field (or MS with 5+ years relevant industry experience);

  • Demonstrated track record applying computational TF ranking and GRN inference to cellular reprogramming problems, transdifferentiation, directed differentiation, or iPSC systems;

  • Multi-platform single-cell RNA-seq expertise: hands-on analysis from at least two different platforms, including platform-specific troubleshooting and quality control;

  • Multi-modal genomics proficiency: ChIP-seq, CUT&RUN, or ATAC-seq analysis including peak calling, differential accessibility, and TF motif enrichment;

  • Hands-on experience with established GRN inference methods to nominate or rank regulators of cell state, beyond literature-curated lists;

  • Experience analyzing pooled perturbation screens (CRISPRa, CRISPR knockout, or barcoded TF overexpression) with single-cell or bulk readouts;

  • Working knowledge of trajectory inference and pseudotime methods for mapping cell state transitions;

  • Strong programming skills in Python and R, with proficiency in Scanpy/Seurat and statistical analysis for high-dimensional data;

  • Comfortable working in a modern computational environment: cloud platforms, workflow managers, containerization, and collaborative version control;

  • Strong publication record and demonstrated cross-functional collaboration with experimental biologists.

Youll stand out with

  • Direct experience nominating or validating TF cocktails that successfully induced a cell state conversion (published or in preparation).

  • Experience with dynamical systems modeling for cell state transitions, or inverse problem approaches for TF combination ranking.

  • Background in advanced trajectory inference (optimal transport, GRN dynamics over pseudotime), Bayesian genomics, multi-omics integration, or cross-species comparative regulatory genomics.

  • Familiarity with transformer architectures in genomics and interest in hybrid classical/ML approaches to gene regulation.

Why e184?

  • Unrivaled impact: Your work directly enables technology that transforms human fertility and reproductive medicine.

  • Full-spectrum growth: Gain exposure to the entire lifecycle of discovery. From screening to mechanistic validation.

  • Best of both worlds: Experience the creative chaos of an early-stage startup with the stability of a well-capitalized company.

  • Elite collaboration: Work alongside a world-class team who are as driven as you are.

What we offer

  • Competitive salary + equity participation is considered

  • State-of-the-art facility in Portland metro area

  • Comprehensive Medical, Dental, Vision, and 401(k) with company match

  • 20 days PTO + 11 paid holidays

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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