ADDITIONAL REQUIREMENTS** - Advanced knowledge of basic statistical principles relevant in medical research - Experience with spatial transcriptomics platforms (10x Genomics Visium, Visium HD, and/or Xenium) - Ability to analyze and solve complex problems and apply quantitative analytical approaches - Demonstrated fluency in one or more programming languages (e.g., R, Python, Perl, Java, C++) and willingness to learn new programming languages as necessary - Familiarity with statistical analytical concepts and methods - Ability to communicate effectively, both in writing and orally - Ability to establish and maintain effective working relationships with employees at all levels throughout the institution - Outstanding customer service skills - Demonstrated commitment and leadership ability to advance diversity and inclusion - Knowledge of basic human anatomy, physiology medical terminology - Ability to interpret and master complex research protocol information - Ability to handle multiple projects simultaneously within rigorous timelines - Potential to work independently with minimal supervision and in a team atmosphere **DESIRED QUALIFICATIONS** - Experience with 10x Genomics Xenium Prime 5K spatial transcriptomics platform - Experience with single-cell RNA-seq analysis and integration with spatial data (Seurat, spacexr/RCTD) \#UWDeptMedicineJobs **Compensation, Benefits and Position Details** **Pay Range Minimum:** $80,244.00 annual **Pay Range Maximum:** $132,612.00 annual **Other Compensation:** - **Benefits:** For information about benefits for this position, visit https://www.washington.edu/jobs/benefits-for-uw-staff/ **Shift:** First Shift (United States of America) **Temporary or Regular?** **Job Description** **The Division of Metabolism, Endocrinology and Nutrition (MET) has an outstanding opportunity for a Research Scientist/Engineer 3 position to support studies within the Pyle and Bjornstad Laboratories.** Housed within the University of Washington Medicine Diabetes Institute at the South Lake Union campus, the Pyle laboratory applies translational data science to bridge complex biostatistics and bioinformatics findings into medical insights.