[Remote] Senior Data Scientist

Note: The job is a remote job and is open to candidates in USA. Risepoint is an education technology company that provides world-class support and trusted expertise to more than 100 universities and colleges. In this role, you will lead AI/ML initiatives that shape student engagement and retention by developing predictive models and driving cross-functional collaborations to enhance student outcomes.

Responsibilities

• 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
• 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
• 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
• 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

Skills

• A proven track record of delivering measurable consumer and business impact through AI/ML initiatives—scoping, managing implementation, and owning outcomes end-to-end
• Experience as a workstream leader: designing, managing, and delivering AI/ML projects in a cross-functional environment
• 5–8+ years in applied machine learning or data science, ideally in education, consumer tech, personalization, or a complex behavioral domain
• Strong background in predictive analytics, recommendation systems, and experimentation (A/B testing, causal inference, uplift modeling)
• Deep expertise in Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, TensorFlow, or PyTorch)
• Experience with Databricks, MLFlow, dbt, and Dagster—or demonstrated ability to ramp quickly on a modern MLOps stack
• Comfort working with complex, multi-source datasets (CRM, LMS, communication logs, speech analytics)
• Excellent communicator across technical and non-technical audiences, including executives; you make the science accessible without losing rigor
• Bachelor's or Master's degree in a technical discipline (computer science, statistics, econometrics, mathematics, or engineering)
• PhD in a technical discipline (not required, but valued)
• Experience in higher education, edtech, or student success platforms
• Familiarity with human-in-the-loop AI systems and responsible ML practices (bias mitigation, model transparency, fairness metrics)
• Prior work building or operationalizing next best action or propensity-to-engage models at scale

Company Overview

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