Principal Data Scientist

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 As a Principal Data Scientist, you will lead data science and machine learning initiatives that improve outcomes for students, university partners, and Risepoint. This is a hands-on technical leadership role. You will work across the data science lifecycle, from defining the problem and building models to experimentation, production deployment, and measuring business impact. You will also help set the technical direction for the team, mentor other data scientists, and work closely with Product, Engineering, Customer Experience, and Partnership teams. How You Will Bring Our Mission to Life What You Will Do Data Science Leadership & Business Impact Lead data science and AI/ML initiatives from problem definition through implementation and measurement. Turn ambiguous business problems into clear data science opportunities, analytical approaches, and measurable outcomes. Own the technical direction of assigned initiatives and make clear recommendations when technical or business tradeoffs arise. Work closely with Product, Engineering, Customer Experience, Partnership, and university partner teams to put data science solutions into practice. Identify new opportunities where machine learning, experimentation, causal inference, optimization, or AI can improve student and business outcomes. Communicate findings and recommendations clearly to technical teams, business partners, and senior leaders. Machine Learning & Advanced Analytics Build and improve predictive models for areas such as enrollment propensity, retention and churn risk, engagement, student success, personalization, and next best action. Choose modeling approaches appropriate to the problem, including supervised and unsupervised learning, recommendation methods, causal inference, and uplift modeling. Develop strong feature engineering and modeling approaches that can be reused across projects. Work with a range of structured and unstructured data, including CRM, LMS, behavioral, communication, and speech analytics data. Evaluate models using appropriate performance, calibration, explainability, and business metrics. Use real-world outcomes to improve models after they are deployed. Maintain strong standards for model validation, documentation, reproducibility, and responsible use of machine learning. …