Director, Data Science

Risepoint

About The Position

Risepoint is an education technology company that provides world-class support and trusted expertise to more than 100 universities and colleges. We primarily work with regional universities, helping them develop and grow their high-ROI, workforce-focused online degree programs in critical areas such as nursing, teaching, business, and public service. Risepoint is dedicated to increasing access to affordable education so that more students — especially working adults — can improve their careers and meet employer and community needs. The Impact You Will Make You will lead the data science behind our Student Journey Platform — a system designed to know what a student needs before they ask, intervene at the moments that matter most, and personalize the experience across every university we serve. This isn't about reactive tooling. It's about building the intelligence layer that orchestrates real-time decisions across the full arc of a student's journey — from first inquiry through completion — and doing it at a scale that no single institution could achieve alone. How You Will Bring Our Mission to Life At Risepoint, data science isn't a reporting function — it's the engine powering predictive AI voice and text experiences that meet students where they are and guide them where they need to go. As Director of Data Science, you'll own the models, evaluation systems, and analytical frameworks that make those experiences smarter, more responsive, and more human over time. Your work won't live in dashboards. It will show up in conversations, decisions, and outcomes for hundreds of thousands of students.

Requirements

A builder's mindset — you create structure from ambiguity and rally people around shared goals
Deep intuition for how predictive and generative AI systems behave in production — and what it takes to keep them performing
The ability to move fluidly between strategic thinking and hands-on technical work without losing altitude
A track record of translating complex models and analyses into narratives that drive executive action
Deep credibility with engineers, product managers, and analysts alike
A genuine investment in developing the people around you
10+ years in data science, machine learning, or analytics roles
5+ years leading multidisciplinary data science or analytics teams
Hands-on experience building or evaluating predictive AI systems in voice or text-based products
Production machine learning experience — model building, launch, governance, monitoring, and lifecycle management
Strong command of Python, SQL, and modern ML frameworks (PyTorch, TensorFlow, scikit-learn)
Experience designing evaluation frameworks for LLM or generative AI features
Proven experience designing and analyzing experiments (A/B testing, feature rollouts, behavioral studies)
Master's or Ph.D. in Computer Science, Data Science, Statistics, or a related field — or equivalent experience

Nice To Haves

Familiarity with generative AI applications in a product or customer experience context or agentic workflow design
Experience with next-best-action, recommendation, or real-time intervention modeling
Familiarity with cloud data platforms and distributed data technologies (Spark, Databricks, etc.)
Background in EdTech, higher education, or other mission-driven industries
Experience partnering with GTM or operations teams, not just product and engineering

Responsibilities

Lead a multidisciplinary team across three interrelated domains: Machine learning models & Lifecycle
Build cutting edge machine learning models and own the end-to-end lifecycle of production ML models powering predictive and conversational AI experiences.
Drive next best action, propensity, and intervention models from discovery through deployment, with governance and monitoring frameworks that ensure they stay accurate and reliable in the wild.
Architect frameworks to extract high-signal insights from voice, text, and behavioral interaction data — spanning classical NLP, large language models, and generative AI techniques — to surface what structured data alone will never reveal.
Integrate disparate signals to build a unified, real-time view of student intent and experience.
Drive the experimentation roadmap (A/B testing, feature rollouts, behavioral studies) with statistical rigor to ensure AI-powered product decisions are backed by hard data.
Define evaluation standards for generative and predictive features, and close the loop between model outputs and product requirements.
Scale a multidisciplinary team by fostering a culture of ownership, analytical rigor, and rapid iteration.
Partner with Product and Engineering to define technical success metrics that translate directly into business ROI.
Distill complex technical outcomes into compelling narratives that drive alignment and action at the leadership level.