WGU is looking to develop decision models that support student experiences throughout the lifecycle, aiming to improve student success and operational efficiency through personalized recommendations and automated solutions.
Requirements
- Strong background with demonstrated results in data science, including supervised and unsupervised learning, model selection, and evaluation.
- Working knowledge of MLOps tools and practices (e.g., CI/CD for ML, model monitoring, model drift detection).
- Moderate experience in data engineering practices, especially around data ingestion, transformation, and orchestration pipelines.
- Ability to map and model decision points with inputs, alternatives, outcomes, and feedback mechanisms.
- Experience incorporating behavioral signals and goals into decision frameworks.
- Proficiency in Python or R and experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
- Experience working with cloud platforms and deploying models in production (e.g., AWS, Azure, GCP).
Responsibilities
- Designs and implements machine learning models that enable recursive learning and support key decision points across the student lifecycle.
- Ensures data inputs and outputs for decision models are structured, connected, and monitored appropriately.
- Partners with Data Engineering to develop data pipelines and operational workflows required to support decision models in production environments.
- Applies best practices in MLOps to monitor, retrain, and update models for sustained relevance and performance.
- Develops dashboards, visualizations, and communication tools that present model insights to non-technical audiences.
- Documents decision models, assumptions, data dependencies, and feedback loops to ensure transparency and reuse.
- Ensures models are interpretable and auditable to align with institutional goals of fairness and accountability.
Other
- Deeply understands requirements, decision points, behavioral or process goals, and success criteria to translate them into model specifications.
- Excellent communication and collaboration skills to bridge technical and non-technical audiences.
- Experience in higher education or a mission-driven environment is a plus.
- Additional travel will be required for College Meetings to support networking toward innovation and thought leadership.
- Bachelor’s degree in quantitative fields such as Computer Science, Data Science, Statistics, Engineering, Behavioral Sciences, or related discipline.
