Senior Data Analyst

Senior Data Analyst Job Details Purpose: The Senior Data Analyst is a senior individual contributor who leads complex healthcare analytics to support strategic clinical, operational, and financial decisionmaking, partnering with leaders and cross‑functional teams to deliver high‑impact insights, advance data governance, and mentor analysts. Responsibilities Strategic Data Analysis and Modeling Analyzes data using standard statistical tools, methods, and techniques; interprets results to identify significant differences and trends. Leads the design and execution of advanced statistical models and predictive analytics. Translates complex business problems into analytical frameworks. Synthesizes, processes, and analyzes large datasets to uncover actionable insights. Presents findings to senior leadership and governance committees. Data Visualization Develops and maintains dashboards and scorecards. Standardizes KPIs and reporting methodologies across departments. Automates enterprise‑level reporting processes. Ensures data visualizations are intuitive, accurate, and aligned with strategic goals. Collaborates with IT to optimize reporting infrastructure. Data Integration, Architecture and Standards Develops and implements databases, data collection systems, and analytics strategies that optimize statistical efficiency and data quality. Leads integration of disparate data sources including EHR, claims, financial, and operational systems. Assists in defining, developing, and implementing data standards and data quality measures. Conceptualizes, creates, and maintains operational and research data models for visualization and statistical analysis. Establishes and maintains standard work procedures governing the appropriate use of data. Stakeholder Leadership and Consultation Serves as a trusted advisor to clinical and administrative leaders. Consults with internal and external customers, including faculty and staff, to identify user needs and clarify data requirements. Provides guidance and interpretation of complex systems and data. Facilitates data strategy sessions and requirements gathering workshops. Builds consensus around data definitions and usage. Mentorship, Documentation, and Innovation Mentors junior and mid‑level analysts on technical and professional development. Documents data analysis efforts, including data sources, reporting specifications, tools used, and issue resolutions. Evaluates and pilots emerging analytics tools and methodologies. Researches and stays up‑to‑date on new technologies and data analysis trends. Leads analytics communities of practice and training sessions. Contributes to scholarly activity. Marginal or Periodic Functions Participates in strategic planning and digital transformation initiatives. Supports grant‑funded research and academic collaborations. Represents the organization at industry conferences and professional forums. Leads post‑implementation reviews of analytics projects. Contributes to vendor evaluations and procurement of analytics tools. Knowledge / Skills / Abilities Strategic Agility Aligns analytics with long‑term organizational goals. Anticipates data needs for emerging initiatives. Advices leadership on strategic risks and opportunities. Dealing with Ambiguity Navigates unclear or evolving data requirements. Makes sound decisions with incomplete information. Adapts analysis to shifting priorities. Influence Builds consensus around data‑driven decisions. Communicates complex findings to diverse audiences. Gains buy‑in for analytics initiatives. Technical Learning Masters new analytics tools and platforms. Applies advanced statistical and machine‑learning techniques. Stays current with industry trends. Innovation Management Triggers new technologies for data processing, data generation (e.g., synthetic), and analysis. Encourages experimentation and learning. Identifies opportunities for automation and AI. Collaboration Leads cross‑functional analytics teams. Shares knowledge and builds analytics capacity. Supports enterprise‑wide data initiatives. Minimum Qualifications Requires a Bachelor's Degree in Data Science, Public Health, Health Information Management, Statistics, Computer Science, or a related field with at least 5 years of experience in healthcare analytics or data science. Skilled in large‑scale data analysis, data processing and data technology. Skilled with one or more industry data models and standards (e.g., OMOP, FHIR, DICOM). Experience with advanced analytics tools, data visualizations, and cloud data environments (e.g., Azure, Google, AWS). Relevant education may substitute for experience on a year‑for‑year basis. Preferred Qualifications Master's Degree in a quantitative or healthcare‑related field with at least 7 years of experience in a complex healthcare environment. Cloud Data Analytics Certification or equivalent cloud‑based certification (Azure, Google, AWS). Salary Range $90,000 + depending on qualifications. Working Conditions Standard office equipment. Repetitive use of a keyboard. May be exposed to occupational hazards such as communicable diseases, blood‑borne pathogens, ionizing and non‑ionizing radiation, hazardous medications, and disoriented or combative patients. Equal Opportunity Employer The University of Texas at Austin, as an equal‑opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment. Background Checks A criminal history background check will be required for finalists under consideration for this position. #J-18808-Ljbffr Phase2 Technology