Decision Scientist

Decision Scientist

If you're passionate about building a better future for individuals, communities, and our country—and you're committed to working hard to play your part in building that future—consider WGU as the next step in your career.

Driven by a mission to expand access to higher education through online, competency-based degree programs, WGU is also committed to being a great place to work for a diverse workforce of student-focused professionals. The university has pioneered a new way to learn in the 21st century, one that has received praise from academic, industry, government, and media leaders. Whatever your role, working for WGU gives you a part to play in helping students graduate, creating a better tomorrow for themselves and their families.

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:

Grade: Technical 408Pay Range: $118,900.00 - $178,500.00

Job Description

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.

What You'll Do

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.

Additional Responsibilities

• Perform other duties as assigned.

What You'll Bring

Required Knowledge, Skills, and Abilities

• 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.

Education

• Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, Behavioral Sciences, Mathematics, or a related quantitative discipline.
• Master's degree preferred.
• 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 at the discretion of the hiring manager.

Experience

• 5+ years of experience in data science, advanced analytics, decision intelligence, or a related field.
• 2+ years of experience designing, deploying, and maintaining machine learning solutions in production environments.
• Experience building and supporting data pipelines and operational workflows that enable scalable analytics and machine learning capabilities.
• Demonstrated success developing decision models, analytical frameworks, or predictive systems that influence human behavior, business outcomes, or customer experiences.