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Spatial Transcriptomics Jobs in California (NOW HIRING)

Transcripta Bio

Computational Biologist

Palo Alto, CA · On-site

Familiarity with spatial transcriptomics or multimodal data integration approaches. * Experience working with or alongside ML/AI teams; familiarity with applying machine learning methods to ...

Mithrl

Bioinformatics Engineer, Pipelines

San Francisco, CA · On-site

$150K - $200K/yr

These workflows include microarray, imaging, spatial transcriptomics, genomics, epigenomics, flow cytometry, and more. This role sits at the center of our technical stack. You will architect Nextflow ...

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Spatial Transcriptomics information

What is spatial transcriptomics?

Spatial transcriptomics is an advanced technique that allows scientists to measure gene expression within the spatial context of tissue samples. Unlike traditional RNA sequencing, which loses information about where each gene is expressed, spatial transcriptomics preserves the physical location of gene activity in tissues. This helps researchers better understand how cells function within their native environments and interact with neighboring cells, which is especially valuable in fields like cancer research, neuroscience, and developmental biology. The method combines microscopy, molecular biology, and computational analysis to produce detailed maps of gene expression.

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

Professionals in spatial transcriptomics often encounter challenges related to handling large, complex datasets and integrating spatial information with gene expression data. Ensuring high-quality sample preparation and mastering advanced imaging or sequencing technologies are also frequent hurdles. These challenges can be addressed by collaborating closely with multidisciplinary teams—including bioinformaticians, molecular biologists, and imaging specialists—and staying up-to-date with the latest software tools and protocols. Continuous learning and effective communication within the team are key to overcoming technical and analytical obstacles in this rapidly evolving field.

What are the key skills and qualifications needed to thrive as a Spatial Transcriptomics Scientist, and why are they important?

To thrive as a Spatial Transcriptomics Scientist, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by an advanced degree in a life science field. Familiarity with spatial transcriptomics platforms (such as 10x Genomics Visium), next-generation sequencing (NGS) technologies, and data analysis tools like R or Python is essential. Strong problem-solving skills, attention to detail, and effective communication are important soft skills for collaborating on interdisciplinary research projects. These skills and qualities are crucial for generating high-quality spatial gene expression data and translating findings into meaningful biological insights.
What are popular job titles related to Spatial Transcriptomics jobs in California? For Spatial Transcriptomics jobs in California, the most frequently searched job titles are:
What job categories do people searching Spatial Transcriptomics jobs in California look for? The top searched job categories for Spatial Transcriptomics jobs in California are:
What cities in California are hiring for Spatial Transcriptomics jobs? Cities in California with the most Spatial Transcriptomics job openings:
Spatial Transcriptomics jobs near you
Scientist II, Clinical Bioinformatics

Scientist II, Clinical Bioinformatics

10x Genomics

Pleasanton, CA • On-site

Other

Posted 28 days ago

Job description

About The Role

10x Genomics is establishing a diagnostics effort, translating our leading single-cell and spatial assay technologies into impactful clinical applications. We are seeking a Scientist II to join the clinical bioinformatics team. The ideal candidate excels at distilling complex biological questions into actionable computational strategies, implementing computational/statistical methods and applying them to large-scale single-cell or spatial transcriptomics datasets to derive clinically meaningful insights.

The role requires a biology-first mindset, proficiency with large-scale bioinformatics analyses, strong scientific acumen and statistical rigor. The successful candidate will have an opportunity to work with some of the largest biomedical datasets assayed using cutting-edge 10x Genomics technologies, deriving clinical insights that power the next generation of clinical diagnostics.

What You Will Be Doing:

  • Implement rigorous computational/statistical methods for single-cell and spatial transcriptomics data analysis.
  • Derive actionable insights from clinical/translational single-cell or in-situ spatial datasets.
  • Design, implement and validate biomarkers for diagnostic applications.
  • Implement and maintain bioinformatics pipelines for reproducible, large-scale data processing.
  • Process and analyze single-cell or in-situ spatial transcriptomics datasets spanning hundreds to thousands of samples.

To Be Successful, You Will Need:

  • Ph.D. in bioinformatics, computational biology, genomics or a related discipline with extensive hands-on experience in single-cell NGS data analysis.
  • A minimum of 2 years of industry experience post Ph.D.
  • Experience analyzing large-scale single-cell or spatial transcriptomics datasets to derive biologically meaningful insights and/or diagnostic biomarkers.
  • In-depth understanding of the assumptions, limitations and caveats of statistical methods.
  • Experience developing and optimizing high-performance, scalable code.
  • Proficiency working in a Linux environment.
  • Goal-oriented, self-motivated and an independent problem solver.
  • Meticulous attention to detail and a conscientious work ethic.

Preferred Skills

  • Hands-on experience with 10x Genomics single-cell and in-situ transcriptomics technologies is a strong preference
  • Hands-on research experience in cancer or autoimmune diseases is a strong preference
  • Knowledge of clinical genomics, biomarker discovery and diagnostics
  • Development of statistical models and algorithms for single-cell or spatial transcriptomics data
  • Application of machine learning, particularly in the context of genomics
  • Proficiency with workflow orchestration frameworks such as Snakemake, Nextflow or Martian
  • Programming best practices including data analysis reproducibility, version control, design patterns, testing, debugging and profiling
  • Track record of writing production-level code or maintaining published software packages
  • High-throughput computing infrastructure such as HPCs or cloud computing

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