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Single Cell Spatial Transcriptomics Jobs (NOW HIRING)

Duke University

Bioinformatician II

Durham, NC · On-site

$94K - $150K/yr

Experience with single-cell and/or spatial transcriptomics analysis * Familiarity with multi-omics data integration * Experience applying machine learning or statistical modeling to biological ...

Duke University

Bioinformatician II

Durham, NC

$94K - $150K/yr

Experience with single-cell and/or spatial transcriptomics analysis * Familiarity with multi-omics data integration * Experience applying machine learning or statistical modeling to biological ...

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How much do single cell spatial transcriptomics jobs pay per hour?

As of Jun 16, 2026, the average hourly pay for single cell spatial transcriptomics in the United States is $21.64, according to ZipRecruiter salary data. Most workers in this role earn between $16.83 and $27.16 per hour, depending on experience, location, and employer.

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

To thrive as a Single Cell Spatial Transcriptomics Scientist, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by an advanced degree (PhD or MSc) in a relevant field. Familiarity with high-throughput sequencing platforms, spatial transcriptomics technologies (like 10x Genomics Visium or NanoString GeoMx), and data analysis tools such as R or Python is essential. Critical thinking, problem-solving, and effective communication are crucial soft skills for interpreting complex data and collaborating in multidisciplinary teams. These skills and qualities are vital for generating reliable insights into cellular function and spatial organization, which drive innovative research and discovery.

What are some typical challenges faced by professionals working in Single Cell Spatial Transcriptomics, and how can they be addressed?

Professionals in Single Cell Spatial Transcriptomics often encounter challenges related to handling large, complex data sets and integrating spatial information with single-cell transcriptomic profiles. These tasks demand strong computational skills and close collaboration with bioinformaticians and other researchers. Effective communication within interdisciplinary teams is essential to ensure experimental design aligns with downstream analysis needs. Staying updated with rapidly evolving technologies and best practices also helps professionals overcome technical hurdles and produce reliable, high-impact results.

What is single cell spatial transcriptomics?

Single cell spatial transcriptomics is a cutting-edge technique that allows researchers to analyze gene expression in individual cells while preserving their spatial location within a tissue. This method combines the high-resolution insights of single-cell RNA sequencing with spatial information, enabling scientists to understand how cells interact and organize within their native environments. It is widely used in biomedical research to study tissue architecture, disease mechanisms, and cellular heterogeneity.
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Single Cell Spatial Transcriptomics jobs near you
Infographic showing various Single Cell Spatial Transcriptomics job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 82% Full Time, and 17% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $45,021 per year, or $21.6 per hour.
Scientist II, Clinical Bioinformatics

Scientist II, Clinical Bioinformatics

10x Genomics

Pleasanton, CA

Other

Posted 19 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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