Bioinformatics Analyst III

Bill: $92.12
Pay: $65-$61
Spread:
Duration: 6 mo

Title:

Bioinformatics Analyst III
Onsite
- Cambridge, MA - 4 days onsite, may be 1 day remote
Through EOY
Top 3 – 5 Skills Needed:
Experience analyzing single-cell transcriptomics and epigenomics data, especially scRNA-seq and scATAC-seq

Experience with single-cell atlas construction and multi-modal data integration
Strong proficiency in Python and bioinformatics/data science workflows
Familiarity with cell type annotation, clustering, trajectory inference, and regulatory analysis
Ability to communicate scientific results clearly and work collaboratively across teams

Job Description:

The successful candidate will work closely with stakeholders across the TIEV (Target Identification and Validation in Immunology Discovery) group to advance the Single-Cell Atlas initiatives. The role will focus on analyzing large-scale single-cell transcriptomic and epigenomic datasets to build reference maps, define tissue-specific niches, and uncover their roles in health and disease.

Key Responsibilities
Curate, harmonize, and analyze large-scale scRNA-seq and scATAC-seq datasets from internal and public sources
Integrate multi-modal data to support single-cell atlas construction
Develop and apply computational and AI/ML methods to classify cell states, identify regulatory networks, and generate disease-relevant insights
Support interpretation of model outputs to better understand cell-state biology, tissue-specific function, and fibroblast heterogeneity
Collaborate with immunology, computational, and cross-functional stakeholders to translate biological questions into computational solutions
Document data curation, processing, and analysis pipelines to ensure reproducibility and transparency
Contribute to time-sensitive projects supporting target discovery and prioritization

Qualifications:

MS degree with 5+ years of experience, or PhD with 0+ years of experience, in a quantitative field such as Bioinformatics, Computational Biology, Computer Science, Computational Genetics, Biostatistics, AI/Machine Learning, or a related discipline
Proficiency in Python and standard ML/data science libraries
Experience working in HPC or cloud environments for large-scale omics datasets
Domain knowledge in single-cell analysis, chromatin accessibility analysis, or systems immunology
Strong attention to detail, documentation, and communication skills
Ability to independently design, execute, and troubleshoot computational workflows
Preferred Technical

Skills:

Experience with Num Py, Pandas, Scikit-learn, Matplotlib, and Seaborn
Familiarity with Tensor Flow and/or Py Torch
Proficiency with Git for version control and collaboration
Hands-on experience with single-cell analysis tools such as Scanpy, Seurat, or Bioconductor
Exposure to multi-modal integration methods such as CITE-seq, ATAC-seq, or spatial transcriptomics
Additional Technical Skills (a plus):

Experience with cell type annotation, clustering, and trajectory inference
Knowledge of regulatory network inference and peak-to-gene linking
Experience building multi-modal AI/ML models that connect transcriptomic, proteomic, and imaging data

Bussiness Unit:

R&D-Discovery (RDDI)

Cost Allocation:

1007|CC|RDI IMM ABC (100026)