Bioinformatics Analyst III

Pay range: 47-57/hr • depending on experience 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 data