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Job Description
About the Role We are seeking a skilled professional to join our team and contribute to the advancement of Single-Cell Atlas initiatives. This role involves analyzing large-scale single-cell transcriptomic and epigenomic datasets to build reference maps, define tissue-specific niches, and explore their roles in health and disease. The successful candidate will collaborate with various stakeholders to translate biological questions into computational solutions. 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. Document data curation, processing, and analysis pipelines to ensure reproducibility and transparency. Contribute to time-sensitive projects supporting target discovery and prioritization.
Education Qualification:
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. Required Skills 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. 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. The Company offers the following benefits for this position, subject to applicable eligibility requirements: medical insurance, dental insurance, vision insurance, 401(k) retirement plan, life insurance, long-term disability insurance, short-term disability insurance, paid parking/public transportation, paid time off, paid sick and safe time, hours of paid vacation time, weeks of paid parental leave, and paid holidays annually - as applicable.
