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Assistant Bioinformatics Computational Biology Jobs

Transcripta Bio

Computational Biologist

Palo Alto, CA · On-site

PhD in Bioinformatics, Computational Biology, Genomics, or a related field with 3+ years of relevant experience in industry. * Extensive hands-on experience processing and analyzing bulk and/or ...

Staffed4U

Biologicial Data Analyst

Chantilly, VA · On-site +1

This position is ideal for individuals with a strong foundation in bioinformatics, computational biology, or genomics who enjoy applying data science and analytical techniques to solve complex ...

Guidehouse

Bioinformatics Scientist

Durham, NC · On-site

Ph.D. in Bioinformatics, Biostatistics, Computational Biology, or related discipline * Minimum of SIX (6) years of experience in bioinformatics, computational biology, or a related scientific ...

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How much do assistant bioinformatics computational biology jobs pay per year?

As of Aug 18, 2026, the average yearly pay for assistant bioinformatics 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 is an assistant bioinformatics computational biology?

Assistant Bioinformatics Computational Biology professionals support research in biology by applying computational techniques to analyze and interpret biological data. They often assist in managing large datasets, developing and running software tools, and collaborating with researchers to solve biological problems. Their work is crucial in fields like genomics, proteomics, and drug discovery, where data analysis is essential. Typically, they have a background in biology, computer science, or a related field and work under the supervision of senior scientists.

What are the key skills and qualifications needed to thrive as an assistant bioinformatics computational biology?

To thrive as an Assistant in Bioinformatics and Computational Biology, you need a solid background in biology, statistics, and computer science, often backed by a relevant degree such as bioinformatics, computational biology, or a related field. Familiarity with programming languages (like Python, R, or Perl), databases, and bioinformatics tools (such as BLAST, Galaxy, or Bioconductor) is typically required. Strong analytical thinking, attention to detail, and effective teamwork skills help you interpret complex data and collaborate on multidisciplinary projects. These skills are essential for accurately analyzing biological data, developing computational solutions, and advancing scientific research.

What are some common challenges faced by assistant bioinformatics computational biology professionals, and how can they be addressed?

Assistant Bioinformatics Computational Biology professionals often encounter challenges such as managing large, complex datasets and keeping up with rapidly evolving analytical tools. Collaborating effectively with interdisciplinary teams—including biologists, clinicians, and software engineers—can also be demanding, as it requires clear communication and an understanding of varied perspectives. To address these challenges, it's helpful to develop strong programming and data management skills, stay updated on current bioinformatics methods, and actively participate in team meetings to ensure alignment on project goals.

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Assistant Bioinformatics Computational Biology jobs near you

Computational Biologist

Transcripta Bio

Palo Alto, CA • On-site

Full-time

Re-posted 24 days ago

Job description

About Transcripta Bio
Transcripta Bio is a preclinical-stage AI drug discovery company pioneering a patient-first approach to therapeutics. Headquartered in Palo Alto, CA, we have built a proprietary closed-loop discovery engine - comprising our Disease Signature Atlas, Drug-Gene Atlas, and Conductor AI platform - that integrates single-cell patient transcriptomics, causal human genetics, and pre-validated chemistry to identify and advance drug candidates with a structural edge over conventional approaches.
WHAT YOU'LL DO
  • Develop, maintain, and optimize reproducible bioinformatics pipelines for processing, QC, and analysis of high-throughput datasets, including bulk RNA-seq, single-cell RNA-seq, and high-content imaging data.
  • Analyze data from drug perturbation screens to identify transcriptomic signatures, compound-gene associations, and patterns of drug response across disease-relevant cell models.
  • Integrate data across multiple experimental modalities (transcriptomics, imaging, protein measurements) to build a coherent picture of biology and prioritize therapeutic hypotheses.
  • Partner with wet lab scientists to help design experiments, define data standards, troubleshoot data quality issues, and ensure clean handoffs between experimental and computational workflows.
  • Contribute to the curation and expansion of the Drug-Gene Atlas: ensure that data inputs are well characterized, analysis methods are calibrated, and outputs are interpretable and reliable.
  • Communicate findings clearly through reports, visualizations, and presentations to both computational and non-computational colleagues.
  • Stay current with advances in transcriptomics, single-cell methods, and computational biology; evaluate and adopt new tools and approaches where they add value.
  • Contribute to code review, documentation, and best practices as the team grows.

WHAT YOU'LL BRING
  • PhD in Bioinformatics, Computational Biology, Genomics, or a related field with 3+ years of relevant experience in industry.
  • Extensive hands-on experience processing and analyzing bulk and/or single-cell RNA-seq data, from raw reads through QC, normalization, dimensionality reduction, clustering, and differential expression.
  • Experience in relevant scientific packages (e.g., scanpy, pandas, numpy, DESeq2, ggplot2) and comfort working in a Linux/command-line environment. Strong programming proficiency in Python and/or R is a plus
  • Experience building and running reproducible workflows using tools such as Snakemake, Nextflow, or equivalent; familiarity with version control (Git) and best practices for collaborative code development.
  • Exposure to high-throughput or perturbational screening datasets (chemical, genetic, or combined) is highly desirable.
  • A biologically grounded mindset: you approach data with mechanistic questions in mind, not just statistical outputs.

NICE TO HAVE
  • Experience analyzing data from functional genomics assays (e.g., ATAC-seq, ChIP-seq, perturb-seq, or pooled CRISPR screens).
  • Familiarity with spatial transcriptomics or multimodal data integration approaches.
  • Experience working with or alongside ML/AI teams; familiarity with applying machine learning methods to biological data.
  • Background in rare genetic disease, neurodegeneration, or other genetically defined disease areas.
  • Experience in cloud-based compute environments (AWS, GCP, or equivalent)

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