Healthcare Data Analyst, Health and Clinical Outcomes Research (Onsite)

University of Texas Medical Branch (UTMB)•Galveston, TX

48d•Onsite

About The Position

Responsible for developing, preparing, interpreting, and monitoring moderate–to–complex health process and outcomes data analyses, projections, data modeling, and reports used by system management in decision-making. Develops, implements, and monitors reporting and analysis solutions from multiple databases to ensure efficient processing, up-to-date tools, improved patient experience, and clinical and operational efficiencies. Serves as an expert resource to provide reporting and analysis guidance and support. Responsible for weekly, monthly, quarterly, annual, and ad-hoc reports design and distribution.

Requirements

Bachelor degree in informatics, information technology, business, data science, or a related field.
Three (3) years of directly-related work experience

Nice To Haves

Master's degree in business administration or related field.
Registered Nurse (RN).
Experience in analysis, quality reporting, or decision support in the healthcare industry.

Responsibilities

Supporting high-impact outcomes and translational research projects that span clinical service lines central to UTMB’s mission.
Conducting data curation, quality review, harmonization, and analysis using Epic Clarity/Caboodle and Epic Cosmos, TriNetX, and Medicare administrative datasets.
Designing and implementing reproducible cohort construction, feature engineering, and phenotype definitions using ICD-10/PCS, CPT/HCPCS, LOINC, RxNorm, and related code systems.
Applying modern statistical and causal-inference methods including time-to-event models, confounding control, target-trial emulation, missing-data strategies, and robustness checks to observational clinical data.
Preparing decision-ready summaries, figures, and narratives for research teams, manuscripts, abstracts, and grant deliverables.
Developing clear visualizations and stakeholder-oriented presentations that make complex findings clinically interpretable.
Integrating clinical timelines with multi-omics datasets (e.g., scRNA-seq, proteomics, pathogen genomics) using privacy-preserving linkage and disciplined study design.
Documenting analytic choices end-to-end with version control and workflow tools so results are traceable and auditable across environments.
Adhering to data governance, HIPAA/IRB requirements, and DUA constraints while enabling responsible data access for collaborators.
Contributing to H-COR’s training culture by sharing templates, code patterns, and documentation that raise the analytic bar across projects.
Manuscript and presentation support.