Bioinformatics Analyst III – Single Cell Genomics (scRNA-seq/scATAC-seq)

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 NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn
• Familiarity with TensorFlow and/or PyTorch
• 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 da