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Transcriptomics Jobs in California (NOW HIRING)

Transcripta Bio

Computational Biologist

Palo Alto, CA · On-site

Integrate data across multiple experimental modalities (transcriptomics, imaging, protein measurements) to build a coherent picture of biology and prioritize therapeutic hypotheses. * Partner with ...

Valius Sciences

Computational Biologist

San Francisco, CA · On-site

$125K - $185K/yr

This coming year, we're on track to scale our patient volume significantly, all while bringing new diagnostic modalities (e.g., single-cell transcriptomics) and analytical approaches into clinical ...

Novartis

Senior Expert I, Data Science

San Diego, CA

$126K - $234K/yr

Integrate multi-modal internal and external preclinical datasets (e.g., genomics, transcriptomics, pharmacology, and functional screens) to produce translationally relevant insights. Apply advanced ...

Aequor

Associate Scientist

South San Francisco, CA · On-site

Experience with single-cell sequencing and spatial transcriptomics is also valuable. Histology experience is preferred but is considered more flexible than NGS experience. The candidate should at ...

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All JobsTranscriptomics JobsTranscriptomics Jobs in California

Transcriptomics information

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

To thrive as a Transcriptomics Scientist, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by an advanced degree in a relevant field. Familiarity with next-generation sequencing (NGS) platforms, RNA-seq analysis pipelines, and programming languages like R or Python is essential. Attention to detail, problem-solving abilities, and effective communication skills set outstanding candidates apart. These competencies are vital for generating accurate transcriptomic data, interpreting complex results, and collaborating within multidisciplinary research teams.

What is transcriptomics?

Transcriptomics is the study of the complete set of RNA transcripts produced by the genome under specific circumstances or in a specific cell. It provides insights into gene expression patterns, how genes are regulated, and how cells respond to various conditions. By analyzing the transcriptome, researchers can better understand biological processes, disease mechanisms, and identify potential targets for therapy. Technologies such as RNA sequencing (RNA-seq) are commonly used in transcriptomics research.

What are some common challenges faced by professionals working in transcriptomics, and how can they be addressed?

Professionals in transcriptomics frequently encounter challenges such as handling large and complex datasets, ensuring data quality, and staying current with rapidly evolving analytical tools and technologies. Working closely with bioinformaticians and statisticians is essential for effective data analysis and interpretation. Additionally, clear communication and collaboration with wet-lab biologists and clinicians help bridge the gap between raw data and meaningful biological insights. Regular training and professional development can help transcriptomics professionals stay updated with the latest best practices and software advancements.

What is the difference between Transcriptomics vs Bioinformatics?

AspectTranscriptomicsBioinformatics
Required credentialsBachelor's or Master's in Biology, Genetics, or related fields; experience with sequencing technologiesBachelor's or Master's in Computer Science, Bioinformatics, or related fields; programming skills
Work environmentLaboratories, research institutions, biotech companiesResearch labs, biotech firms, academic institutions, data analysis centers
Industry usageGenomics, molecular biology, medical researchData analysis, software development, computational biology

While both Transcriptomics and Bioinformatics involve analyzing biological data, Transcriptomics focuses on studying gene expression profiles using sequencing technologies, whereas Bioinformatics encompasses a broader range of computational methods to analyze various biological datasets. Professionals in both fields often collaborate but have distinct skill sets and work environments.

What are popular job titles related to Transcriptomics jobs in California? For Transcriptomics jobs in California, the most frequently searched job titles are:
What job categories do people searching Transcriptomics jobs in California look for? The top searched job categories for Transcriptomics jobs in California are:
What cities in California are hiring for Transcriptomics jobs? Cities in California with the most Transcriptomics job openings:
Transcriptomics jobs near you
Infographic showing various Transcriptomics job openings in California as of July 2026, with employment types broken down into 93% Full Time, 4% Part Time, 1% Temporary, and 2% Contract. Highlights an 93% Physical, 3% Hybrid, and 4% Remote job distribution.

Computational Biologist

Transcripta Bio

Palo Alto, CA • On-site

Full-time

Re-posted 10 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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