RCI-ABBV-31169 Bioinformatics Analyst (Genomic/Multi-omics Data/Python/Bulk RNASeq/Spatial Transcriptomics/Nextflow/Immunology)

Remote Position Title: Data Analyst II Length of Contract: 12 months What are the top 3-5 skills, experience or education requited for this position: • Genomic and multi-omic data analysis, including advanced analyses of single-cell RNASeq, bulk RNASeq, and preferably spatial transcriptomics. Familiarity with statistical approaches in omics. • Fluency in Python and/or R; working in Linux environments; familiarity with workflow management (Nextflow), version control. • Excellent organizational, communication, collaboration, and interpersonal skills. • Experience in immunology or fibrotic diseases especially MASH is a plus. • Educational background: Masters with 3-5, PhD 0-3 years of experience. Job description: • Bioinformatics for Immunology, Genetic and Genomic Sciences (BIGGS) team under the umbrella of Quantitative Medicine and Genomics (QM&G) performs computational research and provides data-driven insights for the immunology discovery and translational programs. • The team is seeking a talented, creative, and motivated individual for a contractor position to perform data integration and analysis of bulk transcriptomics, single-cell omics, and available spatial transcriptomics datasets in an immunology-related disease area, with a focus on liver biology and metabolic-associated liver disease. • The candidate will work in close collaboration with members of BIGGS, Immunology Discovery, and Specialty Development teams to perform omics data curation, integration, and computational analysis. • The successful applicant will employ and develop computational tools and algorithms, integrate and analyze complex multi-omic and single-cell datasets to enable a comprehensive understanding of the disease. • Familiarity with the best practices and cutting-edge tools in single-cell analysis is preferable. The results will drive target validation and in-depth understanding of disease biology, with a direct impact on discovery and translational programs. Key responsibilities include: • Systematically QC, annotate, and integrate public omics and single-cell datasets. • Perform downstream analyses on the integrated datasets; employ cutting-edge tools and analytical approaches to advance the understanding of liver disease biology. • Contribute easily maintainable, robust, and flexible code for analysis pipelines. • Interpret and communicate results to the wider scientific team.