Data Scientist, Department of Internal Medicine (Phoenix)
University of Arizona
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- Location
- Phoenix, United States
- Job type
- Full time
- Posted
- Nov 4
- 1 day ago
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Career path
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About the company
University of Arizona
Higher education · 11 to 50 employees
What you'll do
- Develop, maintain, and optimize computational pipelines for genomic datasets
- Process and analyze datasets from 10x Genomics and related platforms
- Perform quality control, clustering, cell type annotation, and differential gene expression analysis
- Apply machine learning, statistical, and bioinformatics approaches
- Generate figures, visualizations, summaries, and reports
- Work with faculty investigators to interpret results and develop research strategies
- Assist with management, organization, storage, and archival of genomic datasets
- Train students, staff, and investigators in computational analysis methods
What you need
- Strong computational and analytical skills with demonstrated experience in 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 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, and clustering methods
- Ability to communicate complex computational findings and work in a collaborative research environment
- Strong organizational skills, attention to detail, and excellent written and verbal communication skills
Key skills
Linux/UnixBash scriptingRPythonsingle-cellspatial transcriptomicsmachine learningstatistical analysisdimensionality reductionclustering methodsdata visualizationbiological data integrationworkflow automationwritten and verbal communication
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Read the full posting here
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.