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Spatial Transcriptomics Omics Jobs in Boston, MA

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 Boston, MA

Spatial Transcriptomics Omics information

See Boston, MA salary details

$53.2K

$221K

$434.6K

How much do spatial transcriptomics omics jobs pay per year?

As of Aug 4, 2026, the average yearly pay for spatial transcriptomics omics in Boston, MA is $221,048.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,300.00 and $434,600.00 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 are popular job titles related to Spatial Transcriptomics Omics jobs in Boston, MA? For Spatial Transcriptomics Omics jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Spatial Transcriptomics Omics jobs in Boston, MA look for? The top searched job categories for Spatial Transcriptomics Omics jobs in Boston, MA are:
What cities near Boston, MA are hiring for Spatial Transcriptomics Omics jobs? Cities near Boston, MA with the most Spatial Transcriptomics Omics job openings:
Spatial Transcriptomics Omics jobs near you
Infographic showing various Spatial Transcriptomics Omics job openings in Boston, MA as of July 2026, with employment types broken down into 1% Internship, 2% As Needed, 91% Full Time, 1% Part Time, 1% Contract, and 4% Nights. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $221,048 per year, or $106.3 per hour.

Senior Bioinformatics Scientist - III

AllSTEM Connections

Cambridge, MA • On-site

$205K - $221K/yr

Temporary

Medical, Dental, Vision, Retirement

Re-posted 5 days ago

Job description

Must Have Requirements
  • PhD in Genetics, Bioinformatics, Computational Biology, Biostatistics, or a related quantitative field
  • Minimum 5+ years of post-PhD experience in human or complex disease genetics (mandatory)
  • This is a fully onsite role in Cambridge, MA; no remote or hybrid work allowed
  • Candidates must have strong hands-on experience in multi-omics data integration, including:
  • Bulk RNA-seq
  • Single-cell RNA-seq (required)
  • Genotype/GWAS data analysis
  • Spatial transcriptomics (preferred)
  • Proteomics data (e.g., Olink preferred)
  • Strong experience in statistical genetics and transcriptomics analysis
  • Proficiency in R, Python, and Bash, with ability to develop reproducible and scalable workflows
  • Experience working with high-performance computing (HPC) systems
  • Hands-on experience with AWS cloud platforms (e.g., S3, IAM, and related services)
  • Experience working with large-scale external genomic datasets
  • Must have a strong publication record (to be included in resume)
  • Must be willing to relocate at own expense if non-local and join as per project timelines

Key Responsibilities
  • Conduct large-scale statistical genetics analyses to support target discovery and validation
  • Perform genome-wide association studies (GWAS) using population-scale biobank datasets (e.g., UK Biobank, FinnGen, Our Future Health, and other consortium datasets)
  • Develop, implement, and optimize scalable and reproducible computational pipelines for genomic data analysis
  • Perform meta-analysis of genetic association datasets and integrate public and proprietary summary statistics
  • Execute post-GWAS analyses including fine-mapping, colocalization, Mendelian Randomization, transcriptome-wide association studies (TWAS), and polygenic risk scoring (PRS)
  • Integrate and analyze multi-omics datasets such as bulk RNA-seq, single-cell RNA-seq, ATAC-seq, QTLs, and proteomics data for gene and target prioritization
  • Contribute to data integration strategies linking genetics with functional genomics and molecular phenotypes
  • Work closely with cross-functional teams including computational scientists, disease area experts, and wet-lab biologists
  • Stay current with emerging methodologies in statistical genetics, computational biology, and AI/ML applications in genomics
  • Contribute to the interpretation and communication of complex genetic findings to support drug discovery programs

Required Qualifications
  • PhD (or equivalent) in Genetics, Bioinformatics, Computational Biology, Statistical Genetics, Biostatistics, Genetic Epidemiology, or related quantitative field
  • Minimum 5+ years of post-PhD experience in human genetics or complex disease genetics research
  • Strong hands-on experience in GWAS and large-scale genomic data analysis
  • Demonstrated experience in multi-omics data integration, including RNA-seq and/or single-cell RNA-seq
  • Strong programming skills in R, Python, and Bash
  • Experience working in HPC environments and cloud platforms (AWS including S3, IAM, etc.)
  • Strong understanding of reproducible research practices and scalable pipeline development
  • Experience working with large, complex datasets in collaborative research environments
  • Excellent communication skills and ability to work in multidisciplinary teams

Preferred Qualifications
  • Experience with spatial transcriptomics and emerging single-cell technologies
  • Proteomics experience (e.g., Olink or similar platforms)
  • Familiarity with machine learning/AI approaches applied to genomics or multi-omics data
  • Experience in cardiometabolic disease, immunology, neuroscience, or other complex disease areas
  • Strong knowledge of advanced statistical genetics methods including TWAS, Mendelian Randomization, colocalization, fine-mapping, and PRS modeling
Equal Opportunity Employer / Disabled / Protected Veterans
The Know Your Rights poster is available here:
https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12.pdf
The pay transparency policy is available here:
https://www.dol.gov/sites/dolgov/files/ofccp/pdf/pay-transp_%20English_formattedESQA508c.pdf
For temporary assignments lasting 13 weeks or longer, AllSTEM Connections is pleased to offer major medical, dental, vision, 401k and any statutory sick pay where required.
We are committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation for any part of the employment process, please contact your staffing representative who will reach out to our HR team.
AllSTEM Connections participates in the E-Verify program in certain locations as required by law. Learn more about the E-Verify program.
https://e-verify.uscis.gov/web/media/resourcesContents/E-Verify_Participation_Poster_ES.pdf
We also consider for employment qualified applicants regardless of criminal histories, consistent with legal requirements, including, if applicable, the City of Los Angeles' Fair Chance Initiative for Hiring Ordinance. Pursuant to applicable state and municipal Fair Chance Laws and Ordinances, we will consider for employment-qualified applicants with arrest and conviction records, including, if applicable, the San Francisco Fair Chance Ordinance. For Los Angeles, CA applicants: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Additional Skills
(none specified)
AllSTEM Representative Contact Info
Account Executive:
Broughton
Branch Phone:
(909) 244-1777
Location:
Ontario, CA

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