The Impact You Will Make

In this role, you will own the intelligence layer that powers howRisepointengages with students at every stage of their journey. You will lead end-to-end initiativesfrom scoping and design through cross-functional implementation and measurable outcomesthat 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,timelyoutreach. Your decisions willdeterminewho gets reached, when, and howtranslating 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 ownfrom scoping a churn-risk model through deploying it into production and measuring its downstream impacttranslates 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 helpRisepointuniversity 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-endscoping, designing, managing implementation, and driving outcomescoordinating 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 aninitiativelifecycle, from defining success metrics through post-deployment iteration.
• Identifyand scope net-new AI/ML opportunities that deliver impact for students, university partners, andRisepointbusiness, 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 modelsincluding churn risk, engagement propensity, and success likelihoodthat power proactive student outreach and aremonitoredcontinuously 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, andproductionizemodels usingMLOpsframeworks (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 teams 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, anddemonstrablyimproving student outcomes (e.g., reduced churn, higher engagement rates)nd 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 analysiswho 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 teams technicalstandardand mentoring teammates toward greater ownership and impact.

How Impact Will be Measured

• 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.

• Experimentvelocity 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, and cross-functional effectiveness.

What Youll Bring to the Team

Experience That Matters Most

• A proventrack recordof delivering measurable consumer and business impact through AI/ML initiativesscoping, 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 68+ years in applied machine learning or data science, ideally in education, consumer tech, personalization, ora complexbehavioral domain.

• Strong background in predictive analytics, recommendation systems, and experimentation (A/B testing, causal inference, uplift modeling).

• Deepexpertisein Python and SQL;proficiencywith ML libraries (scikit-learn,XGBoost, TensorFlow, orPyTorch).

• Experience with Databricks,MLFlow,dbt, andDagsterordemonstratedability to ramp quickly on a modernMLOpsstack.

• 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.

• Bachelors or Mastersdegree in a technical discipline (computer science, statistics, econometrics, mathematics, or engineering).

Experience Thats Great to Have

• PhD in a technical discipline (notrequired, butvalued).

• 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).
• Priorworkbuilding or operationalizing next best action or propensity-to-engage models at scale.