Skills: The successful candidate will have experience with more than one of the following; analyzing next-generation sequencing (NGS), functional genomics, statistics for big data analysis, or multi-omics data integration Good knowledge of existing bioinformatics databases and file formats In-depth understanding of computational methods for NGS analysis and the usage of public data resources required Strong hands-on skills in relevant programming languages (e.g., R, Python, Shiny, UNIX/Linux, UNIX bash shell scripting, Nextflow), statistical software, cloud computing, visualization tools, and relevant R/Bioconductor packages Demonstrated ability to produce well-designed and documented code Familiarity with computational biology tools and experience working with computational biologists to solve problems. Data Analysis: Implement methods for omics data analysis, and interpretation of genomic data sets (e.g., bulk RNA-seq, scRNA-seq, ATAC-Seq) Data Visualization: Fluency in contemporary data visualization methods like R Shiny, D3 Data Integration: Analyzing diverse datasets (multi-omics) to find relevant drug discovery targets and downstream effects relevant to immune disease Data Mining: Internal, collaborative, and public databases to assist in the characterization of immune disease.