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Risepoint · 3 weeks ago

VP, Marketing Insights & Analytics

United States

Full-time

Remote

Director/Executive

12+ years exp

Risepoint is an education technology company that provides support and expertise to universities and colleges. They are seeking a VP of Marketing Insights & Analytics to lead a unified insights capability that transforms data into strategic intelligence across Marketing & Operations, focusing on revenue growth and operational efficiency.

EducationHigher EducationUniversities

Responsibilities

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

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, Channel Marketing Analytics, Student Success Center (SSC), Integrated Marketing & Partner Insights

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

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

Qualification

Predictive modelingAI/ML applicationsMarketing analyticsOperational analyticsData-driven strategyExperimentation frameworksStakeholder managementBusiness acumenLeadershipCommunicationCross-functional collaboration

Required

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

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