The Senior Scientist will drive computational analysis of high-dimensional datasets, partnering closely with a well-integrated computational-experimental team to generate biological insights from complex genomic data. The ideal candidate has deep expertise in single-cell transcriptomics and epigenomics, experience handling large-scale datasets, and strong quantitative and programming skills. Experience in machine learning and AI approaches is highly desirable. • Lead analysis of single-cell RNA-seq and multiome datasets (joint RNA/ATAC profiling) • Perform integrative analysis across modalities, including bulk RNA-seq, ATAC-seq, and DNA methylation datasets • Develop processing pipelines for novel single cell multiomic technologies • Apply statistical modeling and machine learning methods to identify cellular states, regulatory programs, and epigenetic signatures • Design and implement integrative multi-omic analyses across cohorts and experimental systems • Present findings internally and contribute to publications and grant applications • Stay current with emerging single-cell and AI-driven genomic analysis methodologies • Performs other related duties. • Ph.D in sciences or related field (Computational Biology, Bioinformatics, Genomics, Statistics, Computer Science, or related field) • Minimum five years of experience with scientific investigation. • Must have demonstrated outstanding achievement in a field of research • Must have contributed to a number of peer-reviewed publications, inventions or the like, at least some of which have had a major impact on advancing the field or discipline. • Prior experience successfully securing external grant funding is highly desirable. • Highly Preferred Experience: • Strong experience analyzing bulk and single-cell RNA-seq and epigenomic data • Proficiency in R, including common single-cell analysis frameworks • Experience working with large-scale genomic datasets and high-performance computing environments • Strong statistical background and data visualization skills • Experience analyzing DNA methylation data (e.g., array-based or sequencing-based approaches) • Experience analyzing long-read RNA sequencing datasets • Demonstrated use of machine learning/AI methods for genomic data integration or prediction • Familiarity with cloud-based workflows and reproducible pipeline development • Track record of publications in peer-reviewed journals