Data Science Analyst II

Phase2 Technology; Austin (TX); On-site; USD 72,000 - 88,000; Full time

Job summary

Dormont Manufacturing Co is searching for a Data Science Analyst II based in Austin, Texas. The candidate will develop analytics and machine learning models to enhance enterprise decision-making and mentor junior analysts.

This role involves close collaboration with clinical leaders to solve complex problems using advanced data science and AI methodologies. The position offers a competitive salary starting at $80,000 and requires a Master's degree along with proficiency in Python and SQL.

Qualifications

• Requires a Master’s Degree in a related field with at least 3 years of experience in data science.
• Proficiency in Python or similar language is necessary.
• Strong SQL skills and experience with cloud platforms required.

Responsibilities

• Develop predictive models using advanced machine learning methods.
• Integrate structured and unstructured data from multiple systems.
• Create dashboards and visual tools for data insights.

Skills

Advanced statistical and ML techniques

Python

SQL

Data modeling

Education

Master’s Degree in Data Science, Engineering, Statistics, or related field

Tools

Cloud platforms (Azure, AWS, Google)

ML frameworks

Analytics tools

Job description

Data Science Analyst II

Department: Dell Medical School

Location: UT MAIN CAMPUS

Weekly Scheduled Hours: 40

FLSA Status: Exempt from FLSA

Earliest Start Date: Immediately

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, and 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 years 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.

Preferred Qualifications

• Doctorate in Data Science, Engineering, Computer Science or related field with at least 5 years of experience in applied ML.
• 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 occupational hazards such as communicable diseases, blood borne pathogens, ionizing and non‑ionizing radiation, hazardous medications and disoriented or combative patients, or others.

Background Checks

A criminal history background check will be required for finalists 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 discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. Employees who have access to compensation information as part of their essential job functions must comply with confidentiality obligations.

Employment Eligibility Verification

Upon hiring, applicants must complete the federal Employment Eligibility Verification I‑9 form and present acceptable and original documents proving identity and authorization to work in the United States within the third day of employment. Failure to do so will result in loss of employment.