Senior Data Scientist

Initiative Leadership & Cross-Functional Ownership • Lead AI/ML initiatives end-to-end—scoping, designing, managing implementation, and driving outcomes—coordinating across Product, Engineering, CX, 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, from defining success metrics through post-deployment iteration. • Identify and scope net-new AI/ML opportunities that deliver impact for students, university partners, and Risepoint’s business, and advocate for prioritization with leadership. • Manage relationships with key vendors and software providers as a workstream leader, ensuring delivery commitments are met.

Risepoint is an education technology company that helps regional universities launch and grow online programs for modern learners. Risepoint supports more than 100 universities and colleges across five countries, with programs concentrated in high-demand fields including nursing, healthcare, teaching, business, technology, and public service. Our suite of products and services supports the full student journey and each university’s long-term goals. Together, we increase access to affordable education that delivers a strong return on investment for learners and meets employer and community needs. Learn more at risepoint.com. The Impact You Will Make In this role, you will own the intelligence layer that powers how Risepoint engages with students at every stage of their journey. You will lead end-to-end initiatives—from scoping and design 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 be accountable for delivering the “Next Best Experience” platform: the predictive engine that turns raw behavioral signals into personalized, timely outreach. Your decisions 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 • Lead AI/ML initiatives end-to-end—scoping, designing, managing implementation, and driving outcomes—coordinating across Product, Engineering, CX, 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, from defining success metrics through post-deployment iteration. • Identify and scope net-new AI/ML opportunities that deliver impact for students, university partners, and Risepoint’s business, and advocate for prioritization with leadership. • Manage relationships with key vendors and software providers as a workstream leader, ensuring delivery commitments are met. 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. • Partner with data engineers to ensure clean, reliable pipelines and feature stores that support model development and production deployment at scale. • Work with speech analytics and structured CRM/LMS data to derive behavioral insights across the student lifecycle. 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 teammates and raise the team’s technical bar through code reviews, pair work, and knowledge-sharing. · Model ownership, adaptability, and initiative leadership in a fast-changing environment; set the standard for what it means to own a workstream end-to-end. What Success Looks Like • Predictive models are deployed, monitored, and demonstrably improving student outcomes (e.g., reduced churn, higher engagement rates)—and you can point to specific initiative decisions you made that drove those results. • Cross-functional partners in Product, Engineering, and CX describe you as a leader who owns outcomes, not just analysis—who drives alignment, manages implementation, and delivers results. • Experiment programs are well-designed, velocity is high, and a clear percentage of tests yield statistically significant outcomes that inform production decisions. • The data foundation is materially stronger because of your workstream ownership: pipelines are cleaner, features are better documented, and the team ships faster. • You are actively raising the team’s technical standard and mentoring teammates toward greater ownership and impact. • undefined • Business outcomes tied to model-driven initiatives: retention rates, re-engagement rates, enrollment completion, and conversion lift. • Initiative delivery: on-time scoping, cross-functional execution, and outcome realization against defined success metrics. • Model performance metrics: accuracy, precision, recall, and AUC across deployed models; degradation alerts and retraining cadence. • Experiment velocity and signal rate: number of A/B tests shipped per quarter and percentage yielding statistically significant, actionable results. • Qualitative feedback from Product, Engineering, and CX partners on initiative ownership, communication quality, an