Genomics Data Scientist - Single-Cell & Spatial Transcriptomics

UNIVERSITY OF ARIZONA; Phoenix, Northern (AZ, KY); Hybrid; USD 76,000 - 98,000; Full time

Benefits offered by this job

Full benefits

Tuition reduction for employee and/or家

Job summary

The University of Arizona seeks a Research Data Scientist in the Department of Internal Medicine (Phoenix) to support cutting-edge biomedical research focused on genomics, single-cell biology, and spatial transcriptomics. The role emphasizes computational analyses of multi-omics datasets and development of reproducible pipelines leveraging HPC resources.

The successful candidate will collaborate with Dr. Wondisford and Dr.

Qualifications

• Master's degree or higher in Bioinformatics/Computational Biology, Genomics, or related field.
• Strong experience with large-scale genomic/transcriptomic data analysis.
• Proficiency with Linux/Unix, scripting, and reproducible workflows.

Responsibilities

• Develop and optimize pipelines for scRNA-seq, snRNA-seq, and spatial transcriptomics analyses.
• Process and analyze datasets from 10x Genomics platforms and related technologies.
• Apply ML/statistical methods to derive biological insights and generate testable hypotheses.
• Produce publication-quality figures and summaries for manuscripts and grants.
• Collaborate with investigators across experimental design, data interpretation, and manuscript preparation.
• Assist with data management, storage, and coordination with sequencing facilities.

Skills

Genomics data analysis

Linux/Unix

Bash scripting

R programming

Python programming

Single-cell/spatial transcriptomics

Machine learning

High-performance computing

Scientific visualization

Education

Master's degree or PhD in Bioinformatics/Computational Biology

Bachelor's degree in a related field

Tools

Seurat

Scanpy

scvi-tools

CellChat

Monocle

Harmony

Azimuth

SingleR

Job description

The University of Arizona seeks a Research Data Scientist in the Department of Internal Medicine (Phoenix) to support cutting-edge biomedical research focused on genomics, single-cell biology, and spatial transcriptomics. The role emphasizes computational analyses of multi-omics datasets and development of reproducible pipelines leveraging HPC resources.

The successful candidate will collaborate with Dr. Wondisford and Dr.