Senior Data Scientist

Job Summary

Under the general direction of the Computational and Data Scientist Supervisor, the Senior Data Scientist supports strategically important initiatives within UC Davis Health's Health Analytics Core (HAC). This role focuses on applying advanced computational and analytical methods—including artificial intelligence, machine learning, deep learning, time‑series modeling, and large language models—to develop, evaluate, and translate data‑driven solutions into clinical, operational, and research environments.

The Senior Data Scientist leads and contributes to a diverse portfolio of cutting‑edge, collaborative data science projects. Responsibilities include delivering direct analytical and modeling support for HAC initiatives, providing consultative expertise for projects across UC Davis Health, and designing scalable, reusable data science infrastructure, methodologies, software, and tools. These reusable assets form the foundation for the systematic and sustainable application of data science across UC Davis Health.

The Senior Data Scientist adapts and advances statistical, machine learning, and computational techniques to address complex, real‑world healthcare challenges—often in settings where established analytical approaches do not yet exist. This role drives data‑enabled, multidisciplinary initiatives and requires ongoing learning, innovation, and problem solving in close collaboration with clinicians, operational leaders, and researchers across UC Davis Health and the UC Davis campus.

The Senior Data Scientist also partners closely with teams in UCDH Information Technology, including the Data Provisioning Core, Health Computing Core, and application development teams, to operationalize and deploy analytics solutions into production environments.

Apply By Date:
January 23, 2026 at 11:59pm. Selection activities may start at any time.

• Bachelor's degree in a data‑analytic or equivalent discipline (e.g., Data Science, Statistics, Computer Science, Mathematics, Engineering, Information Science, or a data‑driven disciplinary field) and / or equivalent experience / training.
• 3 years' experience in healthcare data science or comparable field.
• Hands‑on data science problem solving with real‑world, complex data sets.
• Experience applying machine learning, predictive analytic, time series analysis, natural language process, and other advanced analytic and statistical techniques to model complex systems.
• Verbal and written communication skills for research, technical, and lay audiences.
• Problem‑solving and data manipulation skills including the ability to interpret complex healthcare data and aggregate, reorganize, and pivot data to find new and meaningful insights.
• Fluency in the Python programming language and competency using Python's standard machine learning libraries, and a willingness to learn (if necessary) other programming languages such as R and SQL.
• Competency with code management best practices including the use of common code management applications (e.g.
  
  - Github).
• Initiative to advance skills and knowledge as needed to fulfill assigned tasks and projects, including learning new methods and technologies.
• Ability to work both as part of a team and independently, including sharing code, documentation and conducting reproducible analyses.
• Familiarity with project management and process improvement principles, concepts, and methodologies.
• Strong skills in communication of analytic methods and results including data visualization (Tableau, Power BI, etc.).
• Ability to design and conduct rigorous experiments to ensure the safety, quality, fairness, and robustness of advanced analytics solutions being implemented in the organization.
• Ability to work within deadlines and manage competing priorities as well as remain flexible to meet the overall goals of the department.

• Masters or Ph.D. in a data‑analytic discipline, or in a disciplinary field with significant data science background.
• Experience with software development, version control, unit testing, portability including best practices in software engineering/programming for reproducibility.
• E…