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! 

JOB SUMMARY
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 advanced expertise in one or more of the following areas: bioinformatics, quantitative image analysis, or machine learning. Collaborate with the Principal Investigator, laboratory scientists, clinicians, and multidisciplinary partners to design rigorous analytic strategies; analyze complex biomedical datasets within the candidate’s area of expertise; and deliver reproducible analyses, publication-quality figures, manuscripts, presentations, and grant materials. Candidates are not expected to have expertise in all three areas or in every data type used by the laboratory.

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.

LEVI LABORATORY RESEARCH PROGRAM

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.

• Traumatic heterotopic ossification and aberrant mesenchymal differentiation after burns, musculoskeletal trauma, surgery, and tendon injury.
• Fibrosis, wound healing, burn and scar biology, including inflammatory and epigenetic regulation of keratinocyte, fibroblast, immune, and progenitor-cell function.
• Tendon, muscle, bone, and soft-tissue repair, including mechanotransduction, extracellular matrix remodeling, and stem or progenitor-cell fate.
• Neurovascular and neuroimmune regulation of regeneration, tendinopathy, heterotopic ossification, pain, osteosarcoma, and adrenergic signaling.
• Lymphatic regulation of inflammation, tissue repair, and bone resorption, including VEGF-C or VEGF-D signaling and lymphatic endothelial-cell biology.
• Immunometabolism and obesity-related injury responses, including lipid metabolism, inflammatory signaling, and altered regenerative cell states.
• Circulating mesenchymal progenitor cells and liquid-biopsy approaches for early diagnosis, risk prediction, and treatment monitoring.
• Cancer biology and osteosarcoma, including tumor heterogeneity, tumor microenvironment interactions, angiogenesis, pain, drug response, and pulmonary metastasis.

SPECIFIC JOB DUTIES

• 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 complex biomedical datasets from raw files through final biological and clinical interpretation within one or more areas of expertise: bioinformatics, quantitative image analysis, or machine learning. Relevant datasets may include single-cell or bulk transcriptomic, epigenomic, spatial, proteomic, metabolomic, high-parameter cytometry, imaging, micro-CT, or clinical data. Assignments will reflect the candidate’s expertise and project needs; experience with all listed modalities is not required.
• For bioinformatics-focused projects, perform and document analyses appropriate to the data and research question, such as quality control, normalization, batch correction, cell-type annotation, differential analysis, pathway enrichment, trajectory analysis, gene-regulatory network inference, cell-cell communication modeling, or cross-modality integration.
• For image-analysis-focused projects, develop scalable workflows for relevant imaging data, such as histology, immunofluorescence, multiplex imaging, confocal microscopy, whole-slide imaging, micro-CT, or three-dimensional tissue datasets, including segmentation, feature extraction, spatial quantification, and visualization as appropriate.
• Where relevant to the candidate’s expertise and project goals, integrate experimental data with clinical variables, longitudinal outcomes, public datasets, or multiomic databases to identify biomarkers, mechanisms, therapeutic targets, and clinically meaningful patient subgroups.
• For statistical-modeling or machine-learning-focused projects, develop, validate, and interpret models for disease classification, early detection, risk prediction, treatment response, or 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/or Python using version control and appropriate workflow management and computing resources. Use containerized environments and high-performance or cloud computing where appropriate to project needs.

• Establish and maintain laboratory standards for metadata, data dictionaries, sample tracking, file organization, quality metrics, code review, backup, controlled access, privacy, and FAIR data practices in compliance with institutional and sponsor requirements.
• Partner with experimental scientists to translate computational results into specific validation studies and to ensure that data collection, assay design, and experimental metadata support rigorous downstream analysis.
• Create clear, accurate, and publication-ready figures, dashboards, tables, and data narratives for manuscripts, grants, abstracts, presentations, progress reports, and institutional reviews.
• Lead computational and quantitative sections of manuscripts and grant applications, contribute to methods and results writing, maintain transparent analytic documentation, and support responses to scientific and peer review.
• Present findings at laboratory meetings and national or international scientific forums, communicate limitations and uncertainty clearly, and provide analytically sound recommendations to research teams.
• Mentor trainees and staff in study design, statistics, computational biology, reproducible research, scientific visualization, and responsible interpretation of complex data.
• Manage multiple concurrent projects, establish priorities and milestones, identify analytic risks early, and move work efficiently from exploratory analysis to validated, publication-quality completion.

PREFERRED TECHNICAL EXPERTISE

• Ph.D. in data science, biostatistics, bioinformatics, computational biology, computer science, biomedical engineering, or a related quantitative or biological field, with substantial experience applying advanced analytics to biomedical research.
• Master’s degree in a related quantitative or biological field and significant relevant experience independently leading complex biomedical data analyses may be considered.
• Bachelor’s degree in a related quantitative or biological field with extensive progressively responsible experience in biomedical data science may be considered.
• Advanced proficiency in R and Python for data analysis, statistics, visualization, and pipeline development, with experience using version control such as Git and maintaining clear, reproducible analytic documentation.
• Demonstrated ability to independently lead complex biomedical analyses in at least one of the following areas: bioinformatics, quantitative image analysis, or machine learning. Depth in one area is sufficient; expertise in all three areas or in multiple data modalities is not required.
• Strong command of statistical inference and experimental design, with knowledge of methods relevant to the candidate’s specialty, such as regression and generalized models, multiple-testing correction, missing-data methods, confounding, batch effects, or predictive-model evaluation, and the distinction between exploratory and confirmatory analyses.
• Ability to translate biological and clinical questions into rigorous analytic plans, explain technical concepts to nontechnical collaborators, and communicate uncertainty without overinterpreting results.
• Demonstrated ability to evaluate emerging computational methods, benchmark their performance, and implement appropriate tools with transparent documentation, testing, and quality controls.
• Candidates should provide a complete publication record, such as ORCID, Google Scholar, or PubMed, and a link to a GitHub profile, code repository, or other portfolio demonstrating reproducible analytic work, if available. A nonconfidential description of relevant analytic contributions and reproducibility practices may be provided when public links are unavailable.
• Experience in one or more areas relevant to the candidate’s specialty, such as single-cell or spatial transcriptomics, metabolomics, proteomics, epigenomics, CyTOF, imaging mass cytometry, multiplex tissue imaging, or quantitative micro-CT. These are examples of preferred experience, not cumulative requirements; single-cell or spatial transcriptomics plus an additional modality is not required.
• Experience managing and integrating large clinical, imaging, or multiomic databases with robust metadata, reproducible provenance, privacy protections, and appropriate access controls. Experience using Linux, shell scripting, high-performance computing, containerized environments, or structured collaborative code review is also preferred.
• Experience developing interpretable machine-learning or statistical-learning models and evaluating performance, calibration, transportability, bias, and biological or clinical validity.
• A record of peer-reviewed publications and documented analysis code or research software demonstrating meaningful contributions to computational, translational, or biomedical research.

PREFERRED DOMAIN EXPERIENCE

• Mesenchymal progenitor and stem-cell biology, lineage tracing, aberrant cell fate, osteogenesis, chondrogenesis, fibrosis, wound repair, or extracellular matrix biology.
• Immune-stromal, neurovascular, lymphatic, or tumor microenvironment analysis, including macrophage states, endothelial populations, sensory and sympathetic nerves, and intercellular signaling.
• Computational analysis of data from preclinical models of burn and musculoskeletal injury, heterotopic ossification, tendon degeneration, obesity or metabolic disease, wound healing, cancer, or metastasis. Hands-on animal work is not required.
• Human translational studies using biospecimens, circulating rare-cell populations, biomarker discovery, diagnostic modeling, imaging, electronic health record data, or longitudinal clinical outcomes.

SCIENTIFIC LEADERSHIP AND PROFESSIONAL COMPETENCIES

• Demonstrated ownership of complex analyses, sound scientific judgment, careful documentation, timely delivery, and the ability to prioritize effectively across multiple collaborative projects.
• Excellent critical thinking, troubleshooting, scientific writing, oral communication, collaboration, and project-management skills.
• Ability to provide constructive analytic leadership, mentor colleagues, improve laboratory data practices, and build productive partnerships among computational, experimental, and clinical teams.
• Commitment to research integrity, reproducibility, responsible authorship, data stewardship, privacy, continuous learning, and sustained engagement with the scientific questions underlying each analysis rather than functioning solely as a data-processing service.

BENEFITS
UT Southwestern is proud to offer a competitive and comprehensive benefits package to eligible employees. Our benefits are designed to support your overall wellbeing, and include:

• PPO medical plan, available day one at no cost for full-time employee-only coverage
• 100% coverage for preventive healthcare-no copay
• Paid Time Off, available day one
• Retirement Programs through the Teacher Retirement System of Texas (TRS)
• Paid Parental Leave Benefit
• Wellness programs
• Tuition Reimbursement
• Public Service Loan Forgiveness (PSLF) Qualified Employer
• Learn more about these and other UTSW employee benefits!

EXPERIENCE AND EDUCATION

Required

• Education
  PhD In Computer Science, Bioinformatics, (Bio)mathematics, (Bio)statistics, Physics, Electrical Engineering, or related field or 
  Master’s Degree M.S. in Computer Science, Bioinformatics, (Bio)mathematics, (Bio)statistics, Physics, Electrical Engineering, or related field or 
  Bachelor’s Degree B.S. with demonstrated experience in broadly defined areas of bioinformatics 
   

• Experience
  2 years of post-graduation experience in data analysis and/or scientific software development with PhD 
  4 years of post-graduation experience in data analysis and/or scientific software development with Master’s degree 
  6 years of post-graduation experience in data analysis and/or scientific software development with Bachelor’s degree 
   

JOB DUTIES

• Analyzes and develops formalisms, algorithms and software for applications in biomedical research.
• Develops and maintains software tools and infrastructure for resolving specific problems.
• Oversees the development, testing and revision of programs in a team with diverse bioinformatics expertise.
• Trains users and other personnel in analyzing, defining and resolving computational problems.
• Performs other duties as assigned.

SECURITY AND EEO STATEMENT

Security
This position is security-sensitive and subject to Texas Education Code 51.215, which authorizes UT Southwestern to obtain criminal history record information.

EEO
UT Southwestern Medical Center is committed to an educational and working environment that provides equal opportunity to all members of the University community. As an equal opportunity employer, UT Southwestern prohibits unlawful discrimination, including discrimination on the basis of race, color, religion, national origin, sex, sexual orientation, gender identity, gender expression, age, disability, genetic information, citizenship status, or veteran status.