Data Science Analyst II

University of Texas at Austin

10d•$80,000•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. This role translates complex healthcare and business problems into scalable data science and machine learning solutions while collaborating closely with clinicians, informaticists, data engineers, and business stakeholders. The Data Science Analyst II develops predictive models, builds automated data pipelines, and delivers actionable insights that support enterprise decision-making and clinical innovation.

Requirements

Master's degree in Data Science, Statistics, Computer Science, Engineering, Health Informatics, or a related field, with at least three (3) years of professional experience in data science, predictive analytics, machine learning, or healthcare analytics.
Experience applying data science and predictive analytics to solve healthcare, clinical, or business problems.
Strong SQL, data modeling, and Python programming skills.
Experience developing ETL pipelines and working with cloud platforms (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.
Applicants must be authorized to work in the United States on a full-time basis without the need for current or future visa sponsorship.

Nice To Haves

Doctorate in Data Science, Computer Science, Engineering, Statistics, Health Informatics, or related field.
Five (5)+ years of applied machine learning or healthcare analytics experience.
Experience supporting provider organizations, academic medical centers, hospitals, or integrated health systems.
Experience working with provider-side healthcare data, clinical workflows, operational healthcare analytics, or population health initiatives.
Experience working 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 directly 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 both technical and non-technical audiences.
Serve as a trusted consultant on data science, predictive analytics, and AI initiatives.
Design, develop, validate, and deploy predictive and machine learning models supporting clinical and operational initiatives.
Perform feature engineering, model evaluation, hyperparameter tuning, and performance monitoring.
Conduct forecasting, trend analysis, anomaly detection, and scenario modeling.
Monitor deployed models for drift and recommend improvements as data changes.
Translate analytical findings into actionable recommendations.
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.
Develop dashboards and interactive reporting tools that support operational and clinical decision-making.
Automate recurring reports and analytical processes.
Maintain consistency of KPIs and enterprise reporting standards.
Create clear visualizations that simplify complex analytical findings.
Lead small-to-medium analytics initiatives from planning through implementation.
Define project milestones, manage priorities, and communicate status updates.
Mentor junior analysts and promote data science best practices.
Collaborate closely with data architects, engineers, informaticists, and clinical leaders to ensure successful implementation.
Evaluate emerging AI, machine learning, and cloud technologies for enterprise adoption.
Monitor model performance and coordinate remediation following data or regulatory changes.
Ensure compliance with HIPAA, security standards, and institutional policies.
Adhere to internal controls and reporting requirements.
Perform related duties as assigned.