Data Science Analyst II
Confirmed on the employer's own hiring board on Nov 7, 2026. First seen by Alion on Aug 13, 2026. The University of Texas at Austin scores B on the Alion truth index.
Job Posting Title:
Data Science Analyst II----
Hiring Department:
Dell Medical School----
Position Open To:
All Applicants----
Weekly Scheduled Hours:
40----
FLSA Status:
Exempt from FLSA----
Earliest Start Date:
Immediately----
Position Duration:
Expected to Continue----
Location:
UT MAIN CAMPUS----
Job Details:
Purpose
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.
A key focus of this position is building a reliable foundation for healthcare analytics by working directly with clinical databases and healthcare data to extract, integrate, clean, validate, and prepare accurate, analysis-ready datasets. Supporting the Department of Pediatrics, this role emphasizes hands-on SQL, relational databases, healthcare data structures, data integration, and automated data pipelines to ensure clinical and research teams have access to reliable information for reporting, operational decision-making, and future advanced analytics initiatives.
This role translates complex healthcare and business problems into scalable data science and machine learning solutions while collaborating closely with clinicians, researchers, 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.
Responsibilities
Clinical & Operational Partnership
- 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.
Advanced Data Science & Predictive Modeling
- 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.
Data Integration & Engineering
- 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 for clinical, operational, and research analytics.
Visualization & Decision Support
- 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.
Project Leadership & Collaboration
- 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.
Marginal or Periodic Functions
- 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.
Knowledge, Skills & Abilities
Technical Expertise
- Strong understanding of predictive analytics, statistics, and machine learning techniques.
- Proficiency in Python, SQL, and modern analytics frameworks.
- Experience developing automated ETL pipelines and maintaining data integrity.
- Experience working with cloud-based analytics environments.
Communication
- Ability to communicate technical concepts to clinical, operational, and executive audiences.
- Strong presentation and stakeholder engagement skills.
- Ability to translate complex analytical findings into actionable recommendations.
Collaboration
- Demonstrated ability to partner effectively with clinicians, researchers, operational leaders, and technical teams.
- Strong business acumen with a collaborative, solution-oriented approach.
- Ability to balance technical feasibility with operational priorities.
Required Qualifications
- 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.
- Relevant education and experience may be substituted as appropriate.
- Applicants must be authorized to work in the United States on a full-time basis without the need for current or future visa sponsorship.
Preferred Qualifications
- 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.
Salary
$80,000+ depending on qualifications.
Working Conditions
- Standard office equipment.
- Repetitive use of a keyboard.
- May be exposed to such occupational hazards as communicable diseases, blood borne pathogens, ionizing and non-ionizing radiation, hazardous medications and disoriented or combative patients, or others.
Required Materials
- Resume/CV
- 3 work references with their contact information; at least one reference should be from a supervisor
- Letter of interest
Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.
Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.
----
Employment Eligibility:
Regular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University-Wide or Open Recruiting, to include both promotional opportunities and lateral transfers. Staff who are promotion/transfer eligible may apply for positions without supervisor approval.----
Retirement Plan Eligibility:
The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length.----
Background Checks:
A criminal history background check will be required for finalist(s) under consideration for this position.
----
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, educational programs and activities, and admissions.
----
Pay Transparency:
The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.
----
Employment Eligibility Verification:
If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.
----
E-Verify:
The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:
- E-Verify Poster (English and Spanish) [PDF]
- Right to Work Poster (English) [PDF]
- Right to Work Poster (Spanish) [PDF]
----
Compliance:
Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031.
The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.