Principal Data Scientist

Risepoint is an education technology company that provides world-class support and trusted expertise to more than 100 universities and colleges. We primarily work with regional universities, helping them develop and grow their high-ROI, workforce-focused online degree programs in critical areas such as nursing, teaching, business, and public service. Risepoint is dedicated to increasing access to affordable education so that more students, especially working adults, can improve their careers and meet employer and community needs. The Impact You Will Make In this role, you will lead the technical outcomes and rigor for both the intelligence layer that powers how Risepoint engages with students at every stage of their journey and the data engineering backbone beneath it, serving as the principal point of accountability for the domain. You will define and lead end-to-end initiatives—from strategy and architecture through cross-functional implementation and measurable outcomes—that directly shape retention, engagement, and enrollment results for thousands of students across more than 100 university partners.  You will shape and execute the technical vision for and delivery of the “Next Best Experience” platform: the predictive engine that turns raw behavioral signals into personalized, timely outreach. You will build alignment across Product, Engineering and business stakeholders on technical approaches. Your decisions, technical expertise and data-driven recommendation  will determine who gets reached, when, and how—translating data science into student outcomes that help working adults succeed in programs that change their lives. You will bring our mission to life by leading initiatives that make the student journey smarter and more human at the same time. Every initiative you own—from scoping a churn-risk model through deploying it into production and measuring its downstream impact—translates directly into a real person getting the support they need before they fall through the cracks. By driving cross-functional alignment and accountability across Product, Engineering, and CX teams, you will help Risepoint’s university partners serve more students more effectively.  How You Will Bring Our Mission to Life What You Will Do 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  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