Data Software Engineer III - Quality and Patient Safety initiative (QPSi)

Classification Title:

Research Software Engineer III

Classification Minimum Requirements:

A Bachelor’s Degree in computer or physical science, statistics, bioinformatics, analytics, or similar field and five years of experience; Master’s Degree in computer or physical science, statistics, bioinformatics, analytics, or similar field and three years of experience; Doctoral Degree in computer or physical science, statistics, bioinformatics, analytics, or similar field and one year of experience.

Job Description:

This Data Software Engineer will be a crucial member of the Quality and Patient Safety initiative (QPSi) at the UF College of Medicine, tasked with developing and maintaining scalable data pipelines, engineering novel features to enhance AI models and extend the scope of the clinical data warehouse, and contributing to the integration of machine learning models to enhance healthcare quality. The role involves collaborating with healthcare professionals, faculty, and data science teams to manage healthcare-related data, ensuring secure, efficient, and scalable data solutions. The Data Software Engineer will be responsible for designing data architectures, automating workflows, and optimizing databases while ensuring compliance with healthcare data regulations. This position requires a high level of technical proficiency in Python (especially with the Pandas python library), experience with system deployment and administration (e.g., Docker, Ansible), database management (SQL/NoSQL), and an understanding of machine learning operations (MLOps).

About This Role:

Feature Engineering and Data Pipeline Development

• Design, develop, and maintain scalable data pipelines to support efficient extraction, transformation, and loading (ETL) processes for large healthcare datasets.
• Design software for parallel processing of high-performance computing (HiPerGator) and high-performance data analytics workflows together with data scientists.
• Deploy data infrastructure and systems on standard Linux servers and the HiPerGator supercomputer.
• Collaborate with data science teams and clinical domain experts to engineer new features from raw data, optimizing for predictive model performance in machine learning applications.
• Create and refine data structures that facilitate effective feature extraction, ensuring the availability of high-quality datasets for AI and analytics workflows.
• Automate repetitive data processing tasks using Python and other scripting tools to ensure consistency, reliability, and scalability of feature engineering efforts.
• Integrate data from multiple sources, ensuring data accuracy and consistency through robust validation and transformation processes.

Data Quality and Management

• Create and execute the organization’s strategy for improving and maintaining research data quality.
• With the Integrated Data Repository (IDR) team, defines and establishes data quality definitions and rules consistent with unit and organizational priorities.
• Support a consistent ontology.
• Define data quality metrics and establish processes to measure and monitor them continuously.
• Manage communications related to data quality, including evaluation and improvement as needed.
• Work with IDR team and IT partners to prevent and remediate identified quality issues using both manual and automated solutions.
• In accordance with the data governance plan, set priorities and strategies for incorporating new sources of data into the IDR based on demand. 

MLOps and Infrastructure Administration

• Implement and manage Machine Learning Operations (MLOps) frameworks to facilitate the continuous integration, deployment, and monitoring of AI models in production environments.
• Use Ansible for infrastructure automation, managing Linux-based systems and ensuring that deployments are reproducible and environments are consistently configured.
• Collaborate with data scientists and UF Health IT to streamline the deployment of machine learning models, creating CI/CD pipelines for model versioning, testing, and deployment.
• Set up monitoring and logging systems to track model performance in production, and identifying and troubleshooting any issues that arise post-deployment.
• With UF Health IT, develop solutions to ensure high availability and scalability of AI models through proactive system administration and infrastructure monitoring, preventing downtime or performance bottlenecks.

Communicating Knowledge and Results

• Using software tools for business intelligence and analytics (e.g. SAP product stack), word processing, spreadsheet, presentation and organizational analyses (e.g. Microsoft Office products), provides reports of analyses to decision makers. Will also be responsible for developing reporting mechanisms via dashboards.
• Lead and contribute to developing manuscripts, abstracts and presentations for dissemination through journals, trade publications and at regional and national venues. 
• Contribute to the QPSi’s education mission and its efforts to develop educational resources on AI and data science in the realm of health care that will be of interest to a wide range of stakeholders.

About COM's Quality and Patient Safety Initiative (QPSi):