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.
Risepoint is seeking a VP of Business Insights & Analytics to build and lead a unified insights capability that transforms data into strategic, actionable intelligence across Marketing & Operations. This leader will integrate analytics from across the organization into a cohesive insights engine-elevating our understanding of student behavior, partner performance, marketing channel ROI, operational efficiency, and cost/revenue opportunities.
This role is responsible for shaping hypotheses, delivering predictive and prescriptive insights, designing experimentation frameworks, driving critical data-driven cost & revenue initiatives, and influencing decision-making across the executive team. While data engineering and governance sit outside of immediate scope, this role will shape data requirements and partner there closely to ensure data is leveraged fully and effectively.
The ideal candidate is a strategic thinker, builder, and end-to-end operator with deep experience implementing analytics solutions-including AI/ML-enabled approaches-that drive material revenue lift, cost efficiency, or measurable performance improvement.
Key Responsibilities
1. Cross-Functional Influence & Strategic Impact
- Serve as the executive-level insights partner to Marketing & Ops leadership (COO, Channel, Integrated Marketing, Student Success, Strategy leaders)
- Shape strategic hypotheses and drive a fact-based approach to identifying revenue growth opportunities, cost savings, operational efficiency gains, and margin expansion.
- Partner with functional leaders to define, prioritize, and implement strategic initiatives and transformation efforts informed by data, ensuring insights translate into measurable business outcomes.
- Translate advanced analytics into compelling narratives that influence executive and board-level decisions; help direct effort and investment across the business.
- Align insights priorities with business needs and overall strategy, ensuring work is focused on the most impactful outcomes.
- Build strong cross-functional relationships to accelerate adoption of insights and drive measurable, sustained improvements.
2. Lead, Build, and Scale a High-Performing Insights Organization and Business Partnership Model
- Lead the integrated analytics partnership across four core business domains. Each area will have some level of dedicated resourcing to shape opportunity areas, establish key metrics and monitoring, and deliver hands-on impact through data-driven efforts:
- Performance Analytics
- Deliver portfolio-level insights that surface revenue opportunities, risk signals, and growth drivers.
- Own investor and board narratives (including investor-facing Monthly Business Review) with rigorous, actionable analysis and clear messaging.
- Drive cross-functional budget process and forecasting across Marketing & Ops
- Channel Marketing Analytics
- Own insights across paid, organic, email, field marketing channels to drive higher ROI and efficiency.
- Lead MMM, attribution & incrementality, audiences/segmentation, testing capabilities that improve channel allocation and performance.
- Student Success Center (SSC)
- Analyze call center productivity, agent performance, voice-of-student signals, and engagement patterns.
- Use AI/ML and conversation intelligence to identify operational levers that improve conversion and retention.
- Integrated Marketing & Partner Insights
- Deliver partner-level insights across funnel performance, portfolio health, and holistic student lifecycle.
- Identify cross-portfolio opportunities to improve partner growth, student outcomes, and commercial performance.
- Define a scalable organizational model with shared methodologies, tooling, and standards of excellence that increase velocity and quality of insights.
- Establish end-to-end processes for generating insights-from data exploration to recommendations that drive action.
- Create a culture of analytical rigor, experimentation, AI-enabled analysis, and business impact.
- Mentor and develop leaders and analysts, elevating technical depth, business acumen, and storytelling.
3. Drive Action Through Advanced Analytics & Modern Modelling Approaches
- Lead the design and implementation of analytics solutions that produce measurable business impact, including:
- Predictive modeling (e.g., value-based bidding, lead scoring, churn/retention/ drop-out)
- AI/ML applications (e.g., learning from voice data, filtering/recommendations)
- Experimentation and incrementality frameworks
- Optimization models (incl. MMM, value-based bidding, incrementality testing)
- Own model governance, monitoring, and performance lifecycle in partnership with data engineering.
- Introduce and embed a test-and-learn culture across marketing and operations to quantify uplift and ROI.
- Use GenAI tools to accelerate insight discovery, automate exploratory analysis, and improve visualization/storytelling.
- Partner with data engineering teams to define analytical data needs (back-end data pipeline across 1st, 2nd, 3rd party data) and ensure teams can leverage high-quality, AI-ready data assets.
Qualifications
Experience
- 12+ years of experience in analytics, insights, data science, or data-driven strategy roles with a track record of driving material business impact.
- Proven ability to design and implement analytics solutions-including predictive modeling, experimentation, and AI/ML applications-that deliver measurable revenue lift, cost efficiency, or performance improvement.
- Deep experience leading multi-disciplinary analytics teams and building new capabilities, operating models, and standards that scale across complex organizations.
- Strong background in marketing analytics, operational analytics, customer lifecycle insights, or channel optimization, with the ability to connect insights to financial outcomes, unit economics, and partner performance.
- Demonstrated success influencing executives and driving insight-to-action, including shaping strategic hypotheses, guiding investment decisions, and elevating analytics in leadership discussions.
- Experience working with modern analytics and AI ecosystems, partnering closely with data engineering, BI, and product/tech teams to ensure high-quality data, AI-ready infrastructure, and resilient measurement frameworks.
- Nice to have: prior experience in Management Consulting or strategy roles supporting executive-level decision-making.
Skills & Competencies
- Demonstrated experience building and operationalizing analytics frameworks that improve revenue, reduce costs, or enhance efficiency-translating advanced methods into solutions embedded in day-to-day decision-making.
- Distinctive ability to translate complex analytics into strategic narratives and executive-ready recommendations that shape direction, investment, and priorities.
- Deep business acumen across marketing, operations, and partner economics, with the ability to connect insights to financial outcomes, unit economics, and operational levers.
- Strong leadership, communication, and stakeholder management skills, with a proven track record of developing talent, building high-performing teams, and driving adoption of insights across complex, cross-functional organizations.
- Comfortable navigating ambiguity and turning open-ended questions into structured, actionable hypotheses and solutions that drive measurable business impact.
- Strong technical foundation in modern analytics techniques including predictive modeling, segmentation, lifetime value, funnel analytics, experimentation, optimization, uplift modeling, and causal inference approaches where randomized testing isn't feasible.
- Hands-on experience applying AI and ML (LLMs & GenAI, NLP, classification, clustering, forecasting, automation) to generate actionable insights, accelerate analysis, and improve operational performance; familiarity with emerging LLMOps practices such as evaluation, monitoring, and prompt governance.
- Fluency in modern data and analytics environments, with experience partnering closely with data engineering and BI teams to design analytics-ready, AI-enabled data assets and ensure high-quality inputs for modeling and insights.
Risepoint is an equal-opportunity employer and supports a diverse and inclusive workforce.
|
Share
Tweet
LinkedIn
