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Bioinformatics Analyst III

Bioinformatics Analyst III

Spectraforce TechnologiesCambridge, Massachusetts, United States
8 days ago
Job type
  • Full-time
Job description

Title : Bioinformatics Analyst III

Duration : 1-2 months

Location : Cambridge, MA

Top 3 - 5 Skills Needed :

1. Implementing, training AI / ML models

2. Data pre-processing for AI compatibility

3. Domain knowledge of bioinformatics

4. Experience working with single cell and spatial data (transcriptomics, proteomics, epigenomics etc.)

5. Ability to communicate results clearly

Job Description :

The successful candidate will work closely with stakeholders across the IPSI (Immune Profiling & Systems Immunology in Immunology Discovery) organization to advance the Single-Cell Fibroblast Atlas initiative. The role will focus on applying AI / ML and advanced computational methods to large-scale single-cell transcriptomic, proteomics and spatial etc. datasets to define fibroblast states, map tissue-specific niches, and uncover their roles in health and disease.

Key Responsibilities

  • Curate, harmonize, and analyze large-scale single-cell and spatial omics datasets (internal and public) with emphasis on fibroblast biology.
  • Develop and optimize predictive AI / ML models to classify fibroblast states, identify regulatory networks, and generate disease-relevant insights.
  • Integrate multi-modal data (scRNA-seq, spatial etc.) to construct a comprehensive fibroblast atlas.
  • Collaborate with other stakeholders.

Impact

By building the Fibroblast Atlas, the candidate will enable target discovery while driving cross-functional projects at the interface of data science, immunology, and translational medicine impacting the portfolio.

Key Responsibilities

  • Support senior analysts and scientists in implementing, training, and troubleshooting AI / ML models tailored to single-cell and spatial omics data.
  • Ingest, clean, and preprocess large-scale single-cell transcriptomic and spatial datasets (public and internal) for single-cell atlas workflows.
  • Collaborate with immunology and computational teams to translate biological questions on fibroblast states, niches, and disease roles into computational solutions.
  • Document data curation, processing, and modeling pipelines to ensure reproducibility and transparency across the atlas project.
  • Assist in interpreting model outputs to generate insights into fibroblast heterogeneity, tissue-specific function.
  • Contribute to time-sensitive projects with critical deliverables, supporting target discovery and prioritization within the fibroblast atlas framework.
  • Qualifications :

  • MS degree (5+ years of experience) or PhD (0+ years of experience) in a quantitative field (Bioinformatics, Computational Biology, Computer Science, Computational Genetics, Biostatistics, AI / Machine Learning, or related discipline).
  • Proficiency in Python and standard ML / data science libraries.
  • Experience working on HPC or cloud environments for large-scale omics and imaging datasets.
  • Domain knowledge in single-cell analysis, spatial omics, or systems immunology, ideally with exposure to fibroblast or stromal cell biology.
  • Strong attention to detail, documentation, and communication skills.
  • Ability to independently design, execute, and troubleshoot computational workflows.
  • Preferred Technical Skills :

  • Experience with NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn.
  • Familiarity with deep learning frameworks (TensorFlow and / or PyTorch).
  • Proficiency with Git for version control and collaboration.
  • Hands-on experience with single-cell data analysis tools (e.g., Scanpy, Seurat, Bioconductor, or equivalent).
  • Exposure to multi-modal integration methods (e.g., CITE-seq, ATAC-seq, proteomics, imaging mass cytometry, spatial transcriptomics).
  • Additional Technical Skills (a plus) :

  • Experience with OpenCV, Scikit-image, or computer vision models for imaging datasets.
  • Knowledge of cell type annotation, clustering, and trajectory inference methods.
  • Experience building multi-modal AI / ML models that link transcriptomic, proteomic, and imaging data.
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