Data Science and Advanced Analytics Intern

AARP•Washington, DC

7h•Hybrid

About The Position

AARP is the nation's largest nonprofit, nonpartisan organization dedicated to empowering people 50 and older to choose how they live as they age. With a nationwide presence, AARP strengthens communities and advocates for what matters most to the more than 100 million Americans 50-plus and their families: health and financial security, and personal fulfillment. AARP also works for individuals in the marketplace by sparking new solutions and allowing carefully chosen, high-quality products and services to carry the AARP name. As a trusted source for news and information, AARP produces the nation's largest-circulation publications, AARP The Magazine and the AARP Bulletin. Data & Analytics leverages business intelligence gained through advanced data analytics in order to maximize AARP's marketing and communications results. This includes using data science, data engineering, predictive modeling, and market analytics to guide data driven decision making. The AARP Data Science & Advanced Analytics team is seeking a curious and creative intern to support ongoing enhancements to Data Science-owned AI/ML and agentic personalization capabilities across our digital channels. This paid internship, anticipated to begin in Summer 2026 with the potential to extend into additional semesters, sits within the Data Science function and focuses on AI-driven personalization, content intelligence, audience engagement analysis, and emerging agentic decisioning initiatives. The intern will help evaluate engagement signals, content effectiveness, and qualitative feedback to support the evolution of AI/ML-powered personalization systems and optimization frameworks. This internship offers hands-on exposure to how analytics, content strategy, audience engagement, and AI work together to improve personalized digital experiences, while supporting Data Science-led AI and agentic personalization initiatives focused on content intelligence, engagement optimization, and AI-assisted decisioning. Ideal candidates will have an interest in media, marketing analytics, audience strategy, communications, digital content, consumer behavior, personalization, and emerging AI technologies, combined with strong analytical thinking, intellectual curiosity, and the ability to connect insights to business outcomes.

Requirements

Currently pursuing an undergraduate or graduate degree in Media & Marketing Analytics, Audience Strategy, Digital Media, Integrated Marketing Communications, Business Analytics, Consumer Strategy, Marketing, Communications, or a related field
Strong interest in digital experiences, consumer engagement, personalization, and emerging AI technologies
Ability to think analytically, synthesize insights, and communicate findings clearly to both technical and non-technical audiences
Regular and reliable job attendance
Effective verbal and written communication skills
Exhibit respect and understanding of others to maintain professional relationships
Independent judgement in evaluation options to make sound decisions

Nice To Haves

Exposure to digital publishing, content marketing, audience development, or marketing analytics
Familiarity with audience engagement metrics, reporting, dashboards, or performance measurement
Interest in personalization, AI/ML, experimentation, recommendation systems, or emerging agentic AI applications
Experience creating presentations, reports, content analyses, or research summaries
Comfort working with both quantitative and qualitative information to identify patterns, opportunities, and insights
Curiosity about how data, content, and technology can work together to improve customer experiences and business outcomes

Responsibilities

Support ongoing improvements to Data Science-owned AI/ML and agentic personalization capabilities
Review personalized content recommendations and engagement trends
Evaluate content relevance, freshness, tone, and audience alignment
Analyze web, app, and email engagement patterns and analytics
Support experimentation and optimization initiatives
Provide structured qualitative feedback to support AI-assisted decisioning
Assist with summaries, presentations, analytics reviews, and insight documentation