Data Scientist, Department of Internal Medicine (Phoenix)

Develop, maintain, and optimize computational pipelines for analysis of single-cell RNA sequencing, single-nucleus RNA sequencing, and spatial transcriptomics datasets. Process and analyze large-scale genomic datasets generated from 10x Genomics Chromium, Visium, Visium HD, Xenium, and related platforms. Perform quality control, clustering, cell type annotation, differential gene expression analysis, trajectory analysis, data integration, and multimodal analyses. Apply machine learning, statistical, and bioinformatics approaches to identify biologically meaningful patterns and generate testable hypotheses. Develop reproducible analysis workflows using Linux-based computing environments, high-performance computing resources, and version-controlled code repositories. Generate publication-quality figures, visualizations, summaries, and reports for manuscripts, grant applications, presentations, and progress reports. Work directly with faculty investigators to interpret results, troubleshoot analyses, and develop data-driven research strategies. Assist with management, organization, storage, and archival of large genomic datasets. Collaborate with laboratory personnel regarding experimental design, sample preparation, sequencing strategies, and downstream analyses. Coordinate data transfer, sequencing submissions, sample tracking, and communication with sequencing and genomics service providers. Contribute to preparation of manuscripts, abstracts, presentations, and extramural grant applications. Train students, staff, and investigators in computational analysis methods and best practices for genomic data analysis. Participate in laboratory meetings, research seminars, and collaborative project discussions. May assist with tissue collection, sample preparation, library construction, spatial transcriptomics workflows, and related laboratory activities as needed. Knowledge, Skills, and Abilities: Strong computational and analytical skills with demonstrated experience in biological, genomic, transcriptomic, or other large-scale scientific data analysis. Proficiency in Linux/Unix operating systems and command-line environments. Experience with Bash scripting and workflow automation. Proficiency in R and/or Python programming for scientific computing and data visualization. Experience with commonly used single-cell and spatial transcriptomics software packages. Knowledge of machine learning, statistical analysis, dimensionality reduction, clustering methods, data visualization techniques and biological data integration approaches. Ability to communicate complex computational findings to investigators with diverse scientific backgrounds, and work effectively in a collaborative multidisciplinary research environment. Ability to manage multiple collaborative projects simultaneously while meeting deadlines. Strong organizational skills, attention to detail, excellent written and verbal communication skills.