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Spatial Transcriptomics Omics Jobs in Quincy, MA

AA2IT

Bioinformatics Scientist - II

Cambridge, MA

$75/hr

Experience: * 5+ years of hands-on experience in multi-omics data analysis and integration. * Proven work with RNA-Seq, single-cell RNA-Seq, genotype data, spatial transcriptomics, and proteomics (e ...

ZS

Patient-Centered Prediction Lead

Boston, MA · On-site

... omics (bulk and single-cell, spatial transcriptomics, methylation, imaging) * Credible as a thought partner with senior R&D leaders on patient-centered prediction topics; able to engage across both ...

ZS

Patient-Centered Prediction Lead

Boston, MA · On-site

... omics (bulk and single-cell, spatial transcriptomics, methylation, imaging) * Credible as a thought partner with senior R&D leaders on patient-centered prediction topics; able to engage across both ...

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All JobsSpatial Transcriptomics Omics JobsSpatial Transcriptomics Omics Jobs in Quincy, MA

Spatial Transcriptomics Omics information

See Quincy, MA salary details

$51.5K

$213.9K

$420.6K

How much do spatial transcriptomics omics jobs pay per year?

As of Aug 5, 2026, the average yearly pay for spatial transcriptomics omics in Quincy, MA is $213,938.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,500.00 and $420,600.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a spatial transcriptomics specialist?

To thrive as a Spatial Transcriptomics Specialist, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by an advanced degree in a relevant field. Familiarity with high-throughput sequencing platforms, spatial omics technologies, and data analysis tools like R or Python is essential, along with experience using laboratory automation systems. Attention to detail, problem-solving abilities, and effective interdisciplinary communication are crucial soft skills for this role. These skills ensure accurate experimental design, robust data interpretation, and successful collaboration in cutting-edge biological research.

What is the difference between Spatial Transcriptomics Omics vs Spatial Transcriptomics Technician?

AspectSpatial Transcriptomics OmicsSpatial Transcriptomics Technician
Required CredentialsAdvanced degrees (Master's/PhD) in molecular biology, genomics, or related fieldsAssociate's or Bachelor's degree in biology, biotechnology, or related fields
Work EnvironmentResearch labs, biotech companies, academic institutionsLaboratories, research facilities, biotech companies
Industry UsageResearch and development, data analysis, method developmentSample preparation, data collection, equipment operation

Spatial Transcriptomics Omics professionals focus on data analysis, method development, and research, often requiring advanced degrees. In contrast, Spatial Transcriptomics Technicians handle sample preparation and operate equipment, typically with a technical diploma or bachelor's degree. Both roles are essential in the spatial transcriptomics industry but differ in responsibilities and qualifications.

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

Professionals working in Spatial Transcriptomics Omics often encounter challenges such as managing and interpreting large, complex datasets, integrating multi-omics data, and keeping pace with rapidly evolving technologies. Effective collaboration with bioinformaticians, pathologists, and laboratory technicians is essential to ensure high-quality results. Staying updated with the latest analytical tools and participating in cross-disciplinary training can help overcome these challenges and enhance both research quality and career growth.

What is spatial transcriptomics in omics research?

Spatial transcriptomics is a cutting-edge technology in the field of omics that allows researchers to measure and map gene expression within the spatial context of intact tissue sections. This means scientists can see which genes are active in specific locations of a tissue, preserving the spatial relationships between cells. By combining spatial information with transcriptomic data, researchers gain deeper insights into tissue organization, cellular interactions, and disease mechanisms. This approach is especially valuable for understanding complex tissues like tumors or brain structures.
What job categories do people searching Spatial Transcriptomics Omics jobs in Quincy, MA look for? The top searched job categories for Spatial Transcriptomics Omics jobs in Quincy, MA are:
What cities near Quincy, MA are hiring for Spatial Transcriptomics Omics jobs? Cities near Quincy, MA with the most Spatial Transcriptomics Omics job openings:
Spatial Transcriptomics Omics jobs near you
Infographic showing various Spatial Transcriptomics Omics job openings in Quincy, MA as of July 2026, with employment types broken down into 2% As Needed, 80% Full Time, 7% Part Time, 6% Temporary, 1% Contract, and 4% Nights. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution, with an average salary of $213,938 per year, or $102.9 per hour.

Bioinformatics Scientist - II

AA2IT

Cambridge, MA

$75/hr

Full-time

Re-posted 19 hours ago

Job description

Senior Bioinformatics Scientist

Location: Cambridge, MA

Pay Rate: $75/HR - 100/HR

Must Have

Education:

  • Ph.D. in Computational Biology or closely related field — no BS/MS-only candidates will be considered.

Experience:

  • 5+ years of hands-on experience in multi-omics data analysis and integration.
  • Proven work with RNA-Seq, single-cell RNA-Seq, genotype data, spatial transcriptomics, and proteomics (e.g., OLINK).

Technical Skills:

  • Strong programming skills in R, Python, and Bash.
  • Experience with HPC systems and AWS Cloud (IAM, S3, etc.).
  • Experience working with tools like STAR, DESeq2, Seurat, scanpy, LeafCutter, etc.

Data Integration:

  • Able to combine various omics data types (gene/protein expression, mRNA splicing, spatial transcriptomics, genotype).

Communication & Collaboration:

  • Strong written/verbal communication.
  • Self-motivated, adaptable, able to juggle multiple priorities in a dynamic setting.

Nice-to-Have (Preferred Qualifications)

  • Experience with real-world data (e.g., patient-derived data, EHR-linked datasets).
  • Familiarity with spatial transcriptomics analysis tools like squidpy, MERFISH, Slide-seq.
  • Background in statistical or population genetics.
  • Multi-omics data integration (especially RNA-Seq, single-cell RNA-Seq).
  • Strong hands-on coding ability in R and Bash.
  • High-performance computing and cloud (especially AWS).

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