Principal Data Scientist

Risepoint

About The Position

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.

Requirements

8+ years of experience in applied data science, machine learning, or a related field, including technical leadership on complex projects.
Strong hands-on experience building predictive models such as propensity, churn, retention, engagement, personalization, recommendation, or other behavioral models.
Strong understanding of machine learning, statistics, feature engineering, and model evaluation.
Experience designing and analyzing A/B tests used to make real product or business decisions.
Experience with causal inference, uplift modeling, incrementality measurement, or related methods.
Strong Python and SQL skills and experience with common ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
Experience taking machine learning models into production and monitoring their performance after launch.
Experience working with Data Engineering or Engineering teams to productionize data science solutions.
Experience with modern data science and MLOps platforms such as Databricks and MLflow, or comparable technologies.
Experience working with large, complex datasets from multiple sources.
Strong judgment about data quality, model validation, monitoring, explainability, and responsible ML.
Experience mentoring data scientists and providing technical direction on projects.
Ability to turn complex technical work into clear recommendations for Product, Engineering, business partners, and senior leaders.
Bachelor's or Master's degree in computer science, statistics, econometrics, mathematics, engineering, or another quantitative discipline, or equivalent relevant experience.

Nice To Haves

PhD in a quantitative or technical discipline.
Experience in higher education, edtech, consumer technology, personalization, or another complex behavioral domain.
Experience building recommendation systems, next-best-action solutions, propensity models, or personalization systems.
Advanced experience with causal inference, uplift modeling, or treatment-effect estimation.
Experience with speech analytics, NLP, conversational data, or other unstructured behavioral data.
Experience integrating ML solutions with CRM, LMS, customer engagement, or marketing platforms.
Familiarity with responsible AI practices, including model transparency, fairness, and bias evaluation.
Experience with AI-assisted data science and software development tools.

Responsibilities

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.
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.
Design and lead A/B tests that measure whether model-driven interventions improve enrollment, retention, engagement, and other outcomes.
Work with Product and business teams to define what should be tested, how success will be measured, and how results will inform decisions.
Use causal inference, uplift modeling, and related approaches when traditional A/B testing is not practical or when treatment effects vary across groups.
Distinguish between statistical significance and meaningful business impact.
Translate experiment results into clear recommendations about whether to launch, change, expand, or stop an approach.
Build production-ready data science solutions using Python, SQL, Databricks, MLflow, and related technologies.
Partner with Data Engineering and Engineering teams to move models into production and make sure the required data and feature pipelines are reliable.
Define model monitoring requirements for performance, data quality, drift, and business outcomes.
Maintain appropriate testing, versioning, documentation, and governance throughout the model lifecycle.
Investigate production model issues and work with engineering partners to resolve data, pipeline, or serving problems.
Contribute to automated testing, CI/CD, and model deployment practices without being expected to own the underlying engineering platform.
Provide technical leadership on complex data science initiatives and help other data scientists work through difficult modeling and measurement problems.
Mentor data scientists through code reviews, modeling reviews, experiment design, pair work, and knowledge sharing.
Help establish practical standards for model development, experimentation, validation, documentation, explainability, and responsible AI.
Introduce new methods and tools when they can meaningfully improve how the team works.
Work with Product, Engineering, and Data Engineering to continuously improve how data science moves from development into production.
Help shape the Data Science and AI roadmap by identifying opportunities and informing technical priorities.