Decision Scientist

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.