Data Science Analyst II

University of Texas at Austin

9d•Onsite

About The Position

The Data Science Analyst II partners with clinical, operational, and administrative leaders to develop advanced analytics, predictive models, and decision-support solutions that improve patient care, operational efficiency, and organizational performance. Supporting the Department of Pediatrics, the role builds a reliable foundation for healthcare analytics by working directly with clinical databases and healthcare data to extract, integrate, clean, validate, and prepare analysis-ready datasets. The position emphasizes hands-on SQL, relational databases, healthcare data structures, data integration, and automated pipelines to provide clinical and research teams with reliable information for reporting, operational decision-making, and future analytics initiatives. The role translates complex healthcare and business problems into scalable data science and machine-learning solutions. It collaborates with clinicians, researchers, informaticists, data engineers, and business stakeholders to develop predictive models, build automated data pipelines, and deliver actionable insights for enterprise decision-making and clinical innovation. The position leads small-to-medium analytics initiatives and may mentor junior analysts.

Requirements

Master's degree in Data Science, Statistics, Computer Science, Engineering, Health Informatics, or a related field, plus at least three years of professional experience in data science, predictive analytics, machine learning, or healthcare analytics; relevant education and experience may be substituted as appropriate.
Experience applying data science and predictive analytics to healthcare, clinical, or business problems.
Strong SQL, data modeling, and Python programming skills.
Experience developing ETL pipelines and working with cloud platforms such as Azure, AWS, or Google Cloud.
Experience collaborating directly with business, operational, clinical, or research stakeholders to develop analytical solutions.
Excellent written, verbal, and interpersonal communication skills.
Strong understanding of predictive analytics, statistics, and machine-learning techniques.
Proficiency in modern analytics frameworks and experience maintaining data integrity.
Experience with cloud-based analytics environments.
Ability to communicate technical concepts to clinical, operational, and executive audiences and present findings effectively.
Ability to collaborate with clinicians, researchers, operational leaders, and technical teams; strong business acumen and a solution-oriented approach.
Ability to balance technical feasibility with operational priorities.
Must be authorized to work in the United States full-time without current or future visa sponsorship.

Nice To Haves

Doctorate in Data Science, Computer Science, Engineering, Statistics, Health Informatics, or a related field.
Five or more years of applied machine-learning or healthcare analytics experience.
Experience supporting provider organizations, academic medical centers, hospitals, or integrated health systems.
Experience with provider-side healthcare data, clinical workflows, operational healthcare analytics, or population health initiatives.
Experience with healthcare datasets and interoperability standards such as OMOP, FHIR, or HL7.
Experience operationalizing machine-learning models using MLOps practices.
Experience developing automated ETL pipelines and cloud-native analytics solutions.

Responsibilities

Partner with clinicians, operational leaders, researchers, and administrative stakeholders to identify analytical opportunities that improve patient care and operational performance.
Translate complex clinical and business questions into scalable analytical solutions.
Present technical findings and recommendations to technical and non-technical audiences; serve as a consultant on data science, predictive analytics, and AI initiatives.
Design, develop, validate, and deploy predictive and machine-learning models for clinical and operational initiatives.
Perform feature engineering, model evaluation, hyperparameter tuning, and performance monitoring.
Conduct forecasting, trend analysis, anomaly detection, and scenario modeling; translate findings into actionable recommendations.
Monitor deployed models for drift and recommend improvements as data changes.
Build and maintain automated ETL pipelines and reproducible analytical workflows.
Integrate structured and unstructured data from multiple enterprise healthcare systems.
Ensure data quality through validation, reconciliation, and testing.
Partner with Data Engineering and IT teams to optimize data architecture and performance.
Use SQL and relational database tools to extract, transform, reconcile, and organize healthcare data into reliable, reusable datasets.
Develop dashboards and interactive reporting tools for operational and clinical decision-making; automate recurring reports and analytical processes.
Maintain consistency of KPIs and enterprise reporting standards and create visualizations that clarify analytical findings.
Lead small-to-medium analytics initiatives from planning through implementation; define milestones, manage priorities, and communicate status.
Mentor junior analysts and promote data science best practices; collaborate with data architects, engineers, informaticists, and clinical leaders.
Evaluate emerging AI, machine-learning, and cloud technologies for enterprise adoption.
Coordinate model remediation following data or regulatory changes; ensure compliance with HIPAA, security standards, institutional policies, internal controls, and reporting requirements.
Perform related duties as assigned.