Bioinformatics Analyst III

The Fountain Group is a national staffing firm and we are currently seeking a ­­­­­­­­Bioinformatics Analyst for a prominent client of ours. This position is in ­Cambridge, MA 02142. Details for the position are as follows:

Bioinformatics Scientist – Single Cell Genomics & AI/ML
Location: Cambridge, MA (Hybrid)

Pay: $61 - $64/hour

Overview

Our client is seeking a highly motivated Bioinformatics Scientist to join an innovative Immunology Discovery team in Cambridge, MA. This role will support cutting-edge Single-Cell Atlas initiatives by analyzing large-scale transcriptomic and epigenomic datasets to build reference atlases, characterize tissue-specific cellular niches, and uncover biological mechanisms that drive health and disease.

The ideal candidate has a strong background in computational biology, single-cell genomics, and Python-based data analysis. Working closely with immunologists, computational scientists, and cross-functional research teams, you will develop scalable computational workflows that generate insights supporting target identification and validation.

Key Responsibilities

• Curate, harmonize, and analyze large-scale scRNA-seq and scATAC-seq datasets from internal and public data sources.
• Build and support comprehensive single-cell atlas initiatives through multi-modal data integration.
• Develop and apply computational and AI/ML methods to classify cell states, identify regulatory networks, and generate disease-relevant biological insights.
• Perform cell type annotation, clustering, trajectory inference, and regulatory analyses to better understand tissue biology and cellular heterogeneity.
• Collaborate with immunology researchers and cross-functional scientific teams to translate biological questions into computational solutions.
• Design, execute, and optimize reproducible bioinformatics pipelines within HPC or cloud computing environments.
• Document data curation, processing, and analytical workflows to ensure reproducibility and transparency.
• Present findings and recommendations to scientific stakeholders while contributing to target discovery and validation efforts.

Required Qualifications

• M.S. degree with 5+ years of relevant experience, or Ph.D. in Bioinformatics, Computational Biology, Computer Science, Computational Genetics, Biostatistics, AI/Machine Learning, or a related quantitative discipline.
• Hands-on experience analyzing single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) datasets.
• Experience with single-cell atlas construction and multi-modal data integration.
• Strong programming skills in Python and standard data science libraries.
• Experience working with HPC clusters or cloud-based environments for large-scale omics data analysis.
• Strong analytical thinking, attention to detail, and excellent written and verbal communication skills.
• Ability to independently design, execute, troubleshoot, and document computational workflows.

Preferred Qualifications

• Experience with Scanpy
  , Seurat
  , Bioconductor
  , or similar single-cell analysis platforms.
• Knowledge of Num Py
  , Pandas
  , Scikit-learn
  , Matplotlib
  , Tensor Flow
  , and/or Py Torch .
• Experience using Git for version control and collaborative software development.
• Familiarity with CITE-seq
  , spatial transcriptomics
  , chromatin accessibility analysis, or other multi-modal technologies.
• Experience with cell type annotation, clustering, trajectory inference, regulatory network inference, and peak-to-gene linking.
• Knowledge of systems immunology, fibroblast biology, or disease biology is a plus.
• Experience developing AI/ML models integrating transcriptomic, proteomic, imaging, or other multi-omics datasets is highly desirable.

Top Skills

• Single-cell transcriptomics and epigenomics (scRNA-seq, scATAC-seq)
• Single-cell atlas construction and multi-modal data integration
• Python and bioinformatics/data science workflows
• Cell type annotation, clustering, trajectory inference, and regulatory analysis
• Cross-functional collaboration and scientific communication

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