Information Technology Intern (Graduate Student)

AARP•Washington, DC

3d•Hybrid

About The Position

AARP is seeking a graduate student for an Information Technology Services (ITS) internship. The ITS team is responsible for AARP's enterprise-wide technology and information security functions, utilizing technologies like cloud computing, automation, artificial intelligence, and machine learning within Agile teams. This paid internship offers the opportunity to apply computer science, technology, business, and people skills to solve challenging technology problems and impact the 50+ community. Interns will work with IT professionals, focusing on critical problems to advance AARP's mission. The internship is set to begin in July 2026 and may be extended. AARP is committed to the intern's growth and continued learning and development.

Requirements

Currently enrolled in a graduate degree program or as post-doctoral students, and must remain academically enrolled throughout the internship.
Pursuing a graduate degree in Data Science, Computer Science with a Data Science concentration, or Applied Statistics.
Advanced problem-solving skills using logic and data.
Critical thinking and analytical skills.
Ability to articulate complex technical issues to those not as experienced.
Willingness and capacity to learn quickly and apply new knowledge.
Proven analytical skills, excellent written and oral communication abilities.
Must be available to participate in the intern program in the summer, working 40 hours a week.
Advanced user of Excel, PowerPoint, and Word.
College-level experience with coding practices and languages (such as Java, C/C++, Python, or others).
Experience with popular business tools (Windows, Office Professional, Visio, SharePoint, etc.).
Regular and reliable job attendance.
Exhibit respect and understanding of others to maintain professional relationships.

Nice To Haves

Foundational experience with Python and basic machine learning concepts (e.g., classification, regression, or simple predictive models).
Exposure to Natural Language Processing (NLP) concepts, such as text classification, keyword extraction, or sentiment analysis.
Familiarity with modern AI tools or frameworks, such as introductory use of models like BERT or similar NLP libraries.
Interest in applying AI to real-world business problems, such as improving IT service processes or analyzing operational data.
Awareness of AI-assisted tools, such as Microsoft Copilot or similar platforms, and curiosity about how they can improve productivity and decision-making.
Interest in building or interacting with lightweight automation or AI agents, such as Copilot Studio, chat-based workflows, or task automation.

Responsibilities

Applying innovative thinking and problem-solving skills to address data-related challenges within the Information Technology Services department.
Meeting with key leaders to learn how data science supports the design, delivery, operation, and promotion of technology solutions that enable AARP to achieve its goals.
Cleaning, preparing, and analyzing datasets to generate actionable insights for IT-related projects.
Contributing to the interpretation of data analysis results and documenting findings in clear, concise reports for stakeholders.
Learning and applying AARP’s Design Thinking framework to understand user needs and achieve results that align with business objectives.
Exploring innovative solutions for data science projects, focusing on user-centered outcomes.
Collaborating with technology stakeholders to deliver data-driven service solutions that improve system security, performance, and availability.
Applying agile principles and practices while working on IT capability and platform team initiatives.
Supporting process improvement efforts, including constructing and deploying process-centric Business Process Management (BPM) solutions.
Developing predictive models using supervised and unsupervised machine learning techniques to solve IT-related challenges, such as predicting ticket escalations or identifying anomalies in system performance.
Evaluating the performance of machine learning models using appropriate metrics (e.g., R², RMSE, F1 scores) and iterating on model improvements.
Completing an end-of-internship project that showcases your knowledge and presenting findings to IT leadership.