Data Scientist, Department of Internal Medicine (Phoenix)

The University of Arizona•Phoenix, AZ

7d•Onsite

About The Position

The University of Arizona College of Medicine – Phoenix, seeks a highly motivated Research Data Scientist to support cutting-edge biomedical research focused on genomics, single-cell biology, and spatial transcriptomics in the Wondisford Laboratory, Department of Internal Medicine. The successful candidate will work with faculty investigators, including members of Dr. Wondisford's laboratory and Dr. Shenfeng Qiu, Director of Spatial Transcriptomics Core Facility, to analyze and interpret large-scale multi-omics datasets generated from diverse biological systems and disease models. This position will play a critical role in advancing research projects involving single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq), spatial transcriptomics, and related genomic technologies. The candidate will develop and implement computational workflows for data processing, quality control, cell type annotation, differential expression analysis, integration of multimodal datasets, machine learning applications, visualization, and biological interpretation. The successful candidate will collaborate closely with investigators throughout the research lifecycle, from experimental design and sample processing through data analysis, figure generation, manuscript preparation, and grant development. While the primary focus is computational analysis, opportunities may exist to participate in wet-lab activities related to tissue collection, sample preparation, library construction, spatial transcriptomics workflows, and coordination of sample submission to external sequencing facilities. The candidate will work in a highly collaborative and interdisciplinary research environment utilizing state-of-the-art single-cell and spatial transcriptomics platforms, including 10x Genomics Chromium, Visium, Visium HD, and Xenium technologies. Visa sponsorship is not available for this positions.

Requirements

Master's degree or equivalent advanced learning attained through professional level experience required.
Minimum of 5 years of relevant work experience, or equivalent combination of education and work experience.
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.

Nice To Haves

Bachelor's degree, Master's degree and/or Ph.D. in Bioinformatics, Computational Biology, Genomics, Biomedical Informatics, Computer Science, Statistics, Systems Biology, Neuroscience, Biomedical Sciences, or a related field.
Experience analyzing single-cell RNA sequencing and/or single-nucleus RNA sequencing datasets.
Experience analyzing spatial transcriptomics datasets generated using 10x Genomics Visium, Visium HD, Xenium, MERFISH, CosMx, or related platforms.
Experience using Seurat, Scanpy, scvi-tools, CellChat, Monocle, Harmony, Azimuth, SingleR, or related software packages.
Experience with machine learning, deep learning, artificial intelligence, or multimodal data integration methods.
Experience utilizing high-performance computing clusters and cloud-based computing environments.
Familiarity with wet-laboratory techniques related to genomics, next-generation sequencing, single-cell technologies, or spatial transcriptomics.
Experience contributing to peer-reviewed publications, grant applications, and collaborative research projects.
Experience developing reproducible computational workflows and software tools for biological data analysis.

Responsibilities

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.