Decision Scientist

About the role

The Decision Scientist plays a key role within the Experiential Product team at WGU. They are responsible for developing decision models that support student experiences throughout the lifecycle. This role involves blending expertise in data science, behavioral/decision science, and data engineering to design, build, monitor, and continuously improve models within the decision intelligence system.

Responsibilities

• Build Intelligent Decision Systems
• Design, develop, and deploy machine learning models that support key decision points throughout the student lifecycle.
• Translate business goals, behavioral objectives, and operational requirements into scalable analytical and machine learning solutions.
• Develop decision frameworks that incorporate inputs, alternatives, outcomes, and continuous feedback loops.
• Apply advanced analytics, experimentation, and causal inference techniques to identify opportunities that improve student experiences and outcomes.
• Partner Across the Organization
• Collaborate with business stakeholders to understand critical decisions, success measures, and desired outcomes.
• Partner closely with Data Engineering teams to build and maintain the data pipelines and workflows required to support production-ready models.
• Communicate findings, recommendations, and model performance to both technical and non-technical audiences.
• Operationalize and Scale Machine Learning
• Deploy, monitor, retrain, and optimize machine learning models using modern MLOps best practices.
• Ensure data inputs, outputs, and model dependencies are properly governed, monitored, and maintained.
• Implement model monitoring processes to detect performance degradation, data drift, and operational issues.
• Maintain high standards for model reliability, scalability, and production readiness.
• Drive Transparency and Responsible AI
• Create dashboards, reporting tools, and visualizations that make complex insights accessible and actionable.
• Document model assumptions, methodologies, data dependencies, and feedback mechanisms to support transparency and reproducibility.
• Ensure models are interpretable, auditable, and aligned with institutional commitments to fairness, accountability, and ethical use of AI.

Qualifications

• Strong expertise in machine learning, statistical modeling, and predictive analytics, including supervised and unsupervised learning techniques.
• Experience selecting, evaluating, and optimizing machine learning models to solve real-world business problems.
• Knowledge of modern MLOps practices, including model deployment, monitoring, retraining, CI/CD pipelines, and drift detection.
• Experience developing and supporting data pipelines, including data ingestion, transformation, orchestration, and workflow automation.
• Ability to model complex decision processes and connect decision outcomes to measurable business objectives.
• Experience incorporating behavioral, operational, or customer-focused signals into analytical frameworks.
• Proficiency in Python or R and hands-on experience with machine learning frameworks such as: Scikit-learn, TensorFlow, PyTorch.
• Experience deploying machine learning solutions in cloud environments such as AWS, Azure, or Google Cloud Platform.
• Proficiency with Git, GitHub, and collaborative software development practices.
• Excellent communication, collaboration, and stakeholder management skills.
• Experience in higher education, student success, healthcare, or another mission-driven environment is preferred.

Skills

• Strong expertise in machine learning, statistical modeling, and predictive analytics.
• Experience in MLOps practices, including model deployment, monitoring, retraining, CI/CD pipelines, and drift detection.
• Experience developing and supporting data pipelines.
• Ability to model complex decision processes.
• Experience incorporating behavioral, operational, or customer-focused signals into analytical frameworks.
• Proficiency in Python or R.
• Experience deploying machine learning solutions in cloud environments.
• Proficiency with Git, GitHub, and collaborative software development practices.
• Excellent communication, collaboration, and stakeholder management skills.

Benefits

WGU offers a comprehensive benefits package including:

• Medical, dental, vision, telehealth and mental healthcare
• Health savings account and flexible spending account
• Basic and voluntary life insurance
• Disability coverage
• Accident, critical illness and hospital indemnity supplemental coverages
• Legal and identity theft coverage
• Retail savings plan
• Wellbeing program
• Discounted WGU tuition
• Flexible paid time off for rest and relaxation
• No need for accrual for flexible paid sick time
• 11 paid holidays
• Other paid leaves, including up to 12 weeks of parental leave

Pay

The salary range for this position is $118,900.00 - $178,500.00, depending on experience and qualifications.

Schedule

This position is full-time, regular, and based in the Raleigh office, 5 days a week. Occasional travel of up to 20% may be required, including attendance at designated company summits (typically one to two per year).