Principal Data Scientist

Initiative Leadership & Cross-Functional Ownership

• Set direction for and lead AI/ML initiatives end-to-end—scoping ambiguous business opportunities, defining the problem and success criteria, designing the technical approach, managing implementation, and driving outcomes—coordinating across Product, Engineering, CX, Partnership, and university partner teams.
• Own accountability for delivering measurable business outcomes from each initiative: retention lift, engagement improvement, enrollment conversion, and pipeline efficiency.
• Drive alignment and decision-making across teams at each stage of an initiative’s lifecycle, resolving moderately complex, cross-functional problems independently and proactively while escalating only when tradeoffs require leadership decision.
• Identify and scope net-new AI/ML opportunities that deliver impact for students, university partners, and Risepoint’s business; frame options, recommend a path forward, and advocate for prioritization with leadership.
• Manage relationships with key vendors and software providers as a workstream leader, ensuring delivery commitments are met.
• Influence peers, managers, and senior stakeholders across BT and adjacent business functions—including Partnership and Customer Experience—by translating technical tradeoffs into business implications and building support for shared decisions without direct authority.
• Model Development & Production Delivery

Data Engineering & Production Automation

• Build and deploy predictive models—including churn risk, engagement propensity, and success likelihood—that power proactive student outreach and are monitored continuously in production.
• Lead the design and implementation of “next best action” logic in close partnership with Product and CX, from logic design through production deployment.
• Prototype, test, and productionize models using MLOps frameworks (Databricks, MLFlow, dbt, Dagster), owning the full model lifecycle.
• Own clean, reliable data pipelines and feature stores that support model development and production deployment at scale, doubling as the data engineer for the workstream.
• Work with speech analytics and structured CRM/LMS data to derive behavioral insights across the student lifecycle.

Data Engineering & Production Automation

• Architect, build, and own scalable, reliable data pipelines and the underlying data infrastructure (lakehouse, warehouse, and feature stores) end-to-end—operating as the team's principal data engineer.
• Design and maintain data models, ELT/ETL workflows, and feature pipelines that serve both analytics and production model-serving needs.
• Take models to production and keep them healthy there: own packaging, deployment, serving, versioning, and the full production lifecycle, including rollback.
• Automate production workflows with orchestration tools (Dagster, Airflow) for scheduling, dependency management, and pipeline reliability.
• Implement CI/CD pipelines and infrastructure-as-code (Terraform, Docker, Kubernetes) to automate testing, deployment, and reproducible environments.
• Build automated monitoring and observability—data-quality checks, model and data drift detection, alerting, and automated retraining triggers—to keep production systems running with minimal manual intervention.
• Own data quality, governance, lineage, and cost/performance optimization across the platform, setting the engineering standards the team builds against.

Experimentation & Performance Accountability

• Design and lead A/B testing programs to measure model-driven impact on retention, engagement, and satisfaction, owning the decision to ship, iterate, or stop.
• Establish feedback loops and real-world performance monitoring frameworks that enable continuous model improvement.
• Translate complex technical findings into clear, executive-ready narratives that drive cross-functional alignment and action.

Team Leadership & Standards

• Mentor data scientists and engineers across the team and raise the organization’s technical bar through code reviews, pair work, and knowledge-sharing.
• Model ownership, adaptability, and technical leadership in a fast-changing environment; set the standard for what it means to own a domain end-to-end.
• Define technical approaches and promote technical best practices across teams, including standards for data lineage, traceability, and explainability that support user trust and regulatory needs.
• Champion a continuous-learning environment, driving adoption of and experimentation with the latest AI-assisted coding and collaboration tools to multiply team velocity.
• Influence the data science and AI roadmap as the technical expert and thought leader to Product and Engineering leadership.