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Transcriptomics Jobs in Massachusetts (NOW HIRING)

MapLight Therapeutics

Clinical Data Manager

Burlington, MA · On-site

$145K - $160K/yr

We combine cutting-edge technologies including optogenetics, in vivo physiology, and spatial transcriptomics to identify novel drug targets and develop effective therapies to address psychiatric ...

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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 does transcriptomics do?

Transcriptomics is a field within molecular biology that studies the complete set of RNA transcripts produced by a genome under specific conditions. Professionals in this area analyze gene expression patterns using tools like RNA sequencing to understand cellular functions and disease mechanisms.

What biology jobs pay over $100k?

In the field of transcriptomics, roles such as senior research scientist, bioinformatics director, and molecular biology manager often have salaries exceeding $100,000 annually. These positions typically require advanced degrees, extensive experience, and skills in data analysis, programming, and laboratory techniques.

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 highest paying job in genetics?

In genetics, roles such as genetic counselors, research directors, and senior clinical geneticists tend to have the highest salaries, often exceeding six figures annually. Positions requiring advanced degrees, specialized skills, and leadership responsibilities typically offer the highest compensation in the field.

What is the highest paying job in bioinformatics?

In bioinformatics, senior roles such as bioinformatics directors, principal scientists, or lead data scientists tend to have the highest salaries, often exceeding $150,000 annually. These positions typically require advanced skills in programming, data analysis, and experience with large-scale genomic or transcriptomic data, along with leadership responsibilities.

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 Massachusetts? For Transcriptomics jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Transcriptomics jobs in Massachusetts look for? The top searched job categories for Transcriptomics jobs in Massachusetts are:
What cities in Massachusetts are hiring for Transcriptomics jobs? Cities in Massachusetts with the most Transcriptomics job openings:
Transcriptomics jobs near you
Computational Biologist

Computational Biologist

University of Massachusetts Medical School

Worcester, MA

$80K - $90K/yr

Full-time

Posted 2 days ago

Job description

The Computational Biologist be part of an interdisciplinary research group combining systems biology, immunology, and human genetics to uncover the mechanisms that drive autoimmune disease. The lab leads large-scale efforts such as the VIGOR family-based vitiligo cohort (bigor.umassmed.edu) and multi-omic studies of lupus and cutaneous autoimmunity, integrating data across molecular, cellular, and clinical scales.

This position will bridge two complementary areas of research:

  1. Molecular systems immunology, involving the analysis of single-cell and spatial transcriptomic, epigenomic, and proteomic datasets to dissect cell states and communication networks in diseased and healthy tissues.
  2. Genetic and longitudinal modeling, integrating genomic variation with real-world longitudinal data—including proteomics, wearable device metrics, survey responses, and clinical measures—to build predictive and causal models of disease initiation and progression.

The ideal candidate combines strong computational and statistical skills with a biological curiosity about how genetic and environmental factors jointly shape immune dysregulation.


Responsibilities

  • Process, analyze, and interpret large-scale datasets including bulk and single-cell RNA-seq, ATAC-seq, proteomics, and spatial transcriptomics.
  • Develop new analysis methods as needed and as they arise during investigations
  • Perform clustering, trajectory inference, and regulatory network reconstruction to define immune cell states and pathways relevant to autoimmune pathogenesis.
  • Work closely with clinicians, immunologists, and experimentalists to formulate biologically grounded hypotheses and computational analyses.
  • Integrate genetic, molecular, and clinical features to identify mediators linking genotype to phenotype using mediation and causal inference frameworks (e.g., Bayesian networks).
  • Combine data from wearable sensors (e.g., Fitbit activity, sleep, heart rate), clinical surveys, and biomarker measurements to model temporal dynamics of disease activity.
  • Present findings in lab meetings, consortium calls, and scientific conferences; contribute to manuscripts and grant proposals.
  • Generate publication-quality figures and interactive visualizations that communicate complex data intuitively.

Required Qualifications

  • Master’s degree in Computational Biology, Bioinformatics, Genetics, Statistics, Physics, Math or a related quantitative field; Ph.D. strongly preferred.
  • 1-3 years of related experience
  • Strong proficiency in R or Python, statistical modeling, and data visualization.
  • Strong understanding of linear models, mixed-effect models, and in general machine learning approaches to complex datasets.
  • Experience working in Unix/Linux environments and using HPC or cloud-based computational resources.

Preferred Qualifications

  • Background in human genetics or clinical genomics, including genotype imputation, association testing, and fine-mapping.
  • Experience with integrative or multi-omic data analysis and familiarity with single-cell and spatial transcriptomic data.
  • Knowledge of causal inference, longitudinal modeling, or Bayesian hierarchical modeling.
  • Exposure to wearable-device or digital-phenotyping datasets and experience linking such data to molecular or clinical outcomes.
  • Understanding of immunology or autoimmune disease biology.
  • Familiarity with containerization (Docker/Singularity), workflow management systems (Snakemake, Nextflow), and reproducible-research practices.

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