Decision Scientist

Decision Scientist

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.

Primary Responsibilities

• 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.

Qualifications Knowledge, Skills, and Abilities

• 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.

Education

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

Experience

• 5+ years of experience in data science, decision intelligence, or analytics, with at least 2 years of 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.

Preferred Qualifications

• Master's degree 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.

This position requires occasional travel of up to 20%, including required attendance at designated company summits (typically one to two per year). Additional travel may include conferences, visits to company locations, and other business-related events as needed. Additional travel may be assigned as needed to support business requirements.