Decision Scientist

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The salary range for this position takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.

At WGU, it is not typical for an individual to be hired at or near the top of the range for their position, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is:

Job Description

Location: Raleigh, North Carolina

Summary

The Decision Scientist has a key role within the Experiential Product team and is responsible for developing decision models that support student experiences throughout the lifecycle. This role blends expertise in data science, behavioral/decision science, and data engineering to design, build, monitor, and continuously improve models within the decision intelligence system that trigger recommendations to students, staff, or faculty to drive actions that improve student success. The Decision Scientist collaborates closely with the decision intelligence product lead, technology lead, business SMEs, software and data engineering, ML Ops, and technology architects to build decision products that support personalized student progress and completion, drive automated solutions for operational efficiency and scale, and ensure that decisions are data-informed, equitable, and actionable.

• Designs and implements machine learning models that enable recursive learning and support key decision points across the student lifecycle.
• Deeply understands requirements, decision points, behavioral or process goals, and success criteria to translate them into model specifications.
• 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.
• Identifies opportunities to apply advanced analytics, causal inference, and experimentation to improve student experiences.
• Performs other job-related duties as assigned.

This job description includes a general representation of job requirements rather than a comprehensive inventory of all required responsibilities or work activities. The contents of this document or related job requirements may change at any time with or without notice.

Qualifications

• 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).
• Familiarity with version control systems and collaborative development (e.g., Git, GitHub).
• Excellent communication and collaboration skills to bridge technical and non-technical audiences.
• Experience in higher education or a mission-driven environment is a plus.

• Bachelor's degree in quantitative fields such as Computer Science, Data Science, Statistics, Engineering, Behavioral Sciences, or related discipline.

Experience

• 5 or more years of experience in data science, decision intelligence, or analytics.
• 2 or more years of specific experience in applied machine learning and data pipeline development.
• Experience in designing data-driven decision frameworks and deploying ML models in production environments.
• Experience designing or working with decision models or frameworks that influence targeted human behaviors.

Experience in lieu of education

Equivalent relevant experience performing the essential functions of this job may substitute for education degree requirements. Generally, equivalent relevant experience is defined as 1 year of experience for 1 year of education and is the discretion of the hiring manager.

Preferred Qualifications

• Master's degree in related field preferred.

Additional Qualifications

• This position is based in the Raleigh office.
• Additional travel will be required for College Meetings to support networking toward innovation and thought leadership.

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Position & Application Details

How to Apply: If interested, an application will need to be submitted online. Internal WGU employees will need to apply through the internal job board in Workday.

Additional Information

Disclaimer: The job posting highlights the most critical responsibilities and requirements of the job. It's not all-inclusive.

Accommodations: Applicants with disabilities who require assistance or accommodation during the application or interview process should contact our Talent Acquisition team at recruiting@wgu.edu.

Equal Employment Opportunity: All qualified applicants will receive consideration for employment without regard to any protected characteristic as required by law.