Data Scientist III, Mineral Metabolism, Levi Lab

WHY UT SOUTHWESTERN?

With over 75 years of excellence in Dallas-Fort Worth, Texas, UT Southwestern is committed to excellence, innovation, teamwork, and compassion. As a world-renowned medical and research center, we strive to provide the best possible care, resources, and benefits for our valued employees. Ranked as the number 1 hospital in Dallas-Fort Worth according to U.S. News & World Report, we invest in you with opportunities for career growth and development to align with your future goals.

Our highly competitive benefits package offers healthcare, PTO and paid holidays, on-site childcare, wage, merit increases and so much more. We invite you to be a part of the UT Southwestern team where you'll discover a culture of teamwork, professionalism, and a rewarding career!

Works under broad direction to provide and oversee the analysis and development of algorithms and software for clinical and/or basic research problems. Oversees and participates in programs to impact data acquisition, management, and analysis in basic, translational, and clinical research, including clinical trials. Perform advanced professional work in support of biomedical and translational research using expertise in data science, biostatistics, computational biology, software development, and scientific visualization.

Collaborate with the Principal Investigator, laboratory scientists, clinicians, and multidisciplinary partners to design rigorous analytic strategies; integrate complex biological, imaging, and clinical datasets; and deliver reproducible analyses, publication-quality figures, manuscripts, presentations, and grant materials.

This position is designed for a senior data scientist who can independently lead the analytic components of multiple research programs from study design and data acquisition through quality control, modeling, biological interpretation, validation, and dissemination. The Data Scientist III will support mechanistic and translational studies of injury, fibrosis, regeneration, aberrant mesenchymal cell fate, wound healing, tumor biology, and therapeutic response. The role is primarily computational, with close collaboration with experimental and clinical teams to ensure that analyses are biologically grounded and directly responsive to clinically important questions.

The Levi Laboratory studies how immune, stromal, vascular, lymphatic, neural, epithelial, and mesenchymal cell populations interact after injury and in disease. The laboratory integrates transgenic and surgical animal models, human biospecimens, molecular and cellular biology, quantitative imaging, clinical data, and multiomic technologies to identify mechanisms, biomarkers, and therapies that promote faithful tissue repair.

• Lead the design of data-driven research strategies with the PI, clinical collaborators, and laboratory scientists, including outcome definition, sample selection, controls, replication, power considerations, data harmonization, batch mitigation, and validation plans.
• Independently analyze single-cell and bulk transcriptomic, epigenomic, spatial, proteomic, metabolomic, high-parameter cytometry, quantitative imaging, micro-CT, and clinical datasets from raw files through final biological and clinical interpretation.
• Perform and document quality control, normalization, batch correction, feature engineering, cell-type annotation, differential analysis, pathway enrichment, trajectory analysis, gene-regulatory network inference, cell-cell communication modeling, and cross-modality integration.
• Develop scalable image-analysis workflows for histology, immunofluorescence, multiplex imaging, confocal microscopy, whole-slide imaging, micro-CT, and three-dimensional tissue datasets, including segmentation, feature extraction, spatial quantification, and visualization.
• Integrate experimental data with clinical variables, longitudinal outcomes, public datasets, and large multiomic databases to identify biomarkers, mechanisms, therapeutic targets, and clinically meaningful patient subgroups.
• Develop, validate, and interpret statistical and machine-learning models for disease classification, early detection, risk prediction, treatment response, and biomarker prioritization using appropriate feature selection, cross-validation, calibration, sensitivity analyses, and external or biological validation.
• Build, document, test, and maintain reproducible analysis pipelines in R and Python using…