Data Science Analyst II

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 is responsible for developing and deploying advanced analytics, machine learning models, and data pipelines to support enterprise and clinical decision‑making. This role partners with clinical and administrative leaders to translate complex business problems into scalable data science and AI solutions, contributes to predictive modeling and automation initiatives, and mentors junior analysts. Working closely with data architects, engineers, informaticians, and clinicians, the Data Science Analyst II helps design and implement innovative analytic solutions—often at the point of care—that drive systemwide performance improvement.

Responsibilities

Advanced Data Science and Modeling

Designs and develops predictive models using advanced ML methods.

Performs feature engineering, model evaluation, and hyperparameter tuning.

Builds and tests prototypes for deployment in clinical or operational workflows.

Conducts scenario modeling, pattern detection, and trend forecasting.

Monitors models for performance and drift

Synthesizes findings into meaningful insights and recommendations.

Data Integration and Pipeline Development

Integrates structured and unstructured data from multiple enterprise systems.

Builds and maintains automated pipelines, ETL processes, and reproducible scripts.

Uses code repositories and CI/CD methods for model and analytics deployment.

Ensures data accuracy through validation and rigorous quality checks.

Partners with IT and data engineering to optimize architecture.

Data Visualization and Decision Support

Develops advanced dashboards and interactive tools.

Automates recurring modeling outputs and analytics workflows.

Ensures consistency of model-driven KPIs across departments.

Creates visualizations that simplify complex findings.

Stakeholder Engagement and Consultation

Serves as a data science consultant to clinical and operational leaders.

Translates ambiguous questions into structured analytical methods.

Leads meetings to gather requirements and present insights.

Guides teams on the interpretation of AI/ML outputs.

Mentorship and Project Leadership

Mentors junior analysts and reviews modeling work.

Leads small-to-medium-sized data science projects.

Defines milestones, tracks progress, and communicates with stakeholders.

Contributes to the development of data science best practices.

Marginal or Periodic Functions:

Evaluates emerging AI/ML tools and cloud technologies to guide enterprise adoption and architecture decisions.

Ensures data science workflows comply with security, HIPAA, and institutional standards through periodic reviews.

Audits and remediates model performance after drift, regulatory changes, or major data-source updates to maintain safe clinical integration..

Adheres to internal controls and reporting structure.

Performs related duties as required.

Knowledge/Skills/Abilities:

Functional/Technical Skills

Demonstrates a strong understanding of advanced statistical and ML techniques.

Applies advanced ML/statistical methods to build predictive models.

Maintains proficiency in Python, SQL, and ML frameworks.

Ensures data integrity across complex pipelines and ETL processes.

Priority Setting

Possesses the ability to manage complex analytical workflows and multiple priorities.

Balances multiple analytics projects and deadlines effectively.

Allocates resources to high-impact modeling initiatives.

Adjusts priorities when urgent clinical needs arise.

Communicating Effectively

Communicates effectively and simplifies technical concepts.

Translates technical findings into actionable insights for leaders.

Creates visualizations that make complex data understandable.

Adapts communication style for technical and non-technical audiences.

Technical Learning

Demonstrates proficiency in cloud-based analytics environments.

Adopts emerging cloud tools and MLOps practices.

Experiments with new ML algorithms and evaluates performance.

Shares new techniques with peers through code reviews and demos.

Peer Relationships

Exhibits a collaborative mindset with strong business acumen.

Partners with IT, clinicians, and administrators on data projects.

Resolves conflicts between technical feasibility and operational needs.

Encourages team knowledge-sharing and joint problem-solving.

Required Qualifications

Requires a Master's Degree in Data Science, Engineering, Statistics, Computer Science, or related field with at least 3 year(s) of experience in data science, machine learning, or predictive analytics.

Proficiency in Python or similar language

Strong SQL and data modeling skills.

Experience with cloud platforms (Azure, AWS, Google).

Familiarity with ML frameworks and analytics tools.

Relevant education and experience may be substituted as appropriate.

Preferred Qualifications

Doctorate in Data Science, Engineering, Computer Science or related field with at least 5 year(s) of experience in applied ML experience.

Experience working with healthcare datasets and standards (OMOP, FHIR).

Experience operationalizing models or using MLOps tools.

Demonstrated experience in ETL, automation, and at least one cloud environment.

Experience with clinical informatics data exchange standards and platforms.

Salary Range

$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.

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.