The University of Florida Lastinger Center for Learning is looking to improve the quality of teaching, learning, and childcare by researching, developing, and scaling educational innovations. This role will focus on leveraging educational technologies and improving the implementation of research-based practices through data analytics and machine learning to support lifelong success for learners.
Requirements
- Extensive experience in machine learning, data engineering, and DevOps practices.
- Strong proficiency in machine learning frameworks and tools such as Azure ML, Snowflake, or PyTorch.
- Expertise in programming languages (Python, SQL) and cloud platforms.
- Demonstrated capability in designing and managing data infrastructure and pipelines.
- Familiarity with agile methodologies and project management frameworks.
- Associate’s degree and two years of relevant experience; or a high school diploma or equivalent and four years of relevant experience. Appropriate college coursework or vocational/technical training may substitute at an equivalent rate for the required experience.
- Bachelor’s degree and five years of appropriate experience.
Responsibilities
- Develop, test, deploy, and monitor machine learning models aimed at improving learner retention, performance, and predictive analytics.
- Develop early-warning models to flag at-risk learners and recommend interventions.
- Create mastery prediction models to support personalized learning paths.
- Design, implement, and maintain scalable data pipelines to support learning analytics projects.
- Perform basic data maintenance and ensure accurate data flow across systems.
- Lead DevOps processes for the Learning Analytics environment.
- Ensuring robust model deployment, performance monitoring, and continuous integration and deployment.
Other
- Provide expert insights and recommendations on the strategic use of analytics and machine learning technologies within educational contexts.
- Collaborate with security and compliance teams to operationalize FERPA aligned data governance practices.
- Collaborate closely with stakeholders to gather, interpret, and document business and technical requirements.
- Regularly communicate complex analytics results to diverse audiences, including non-technical stakeholders.
- Mentor student assistants working on data projects.
