Advisory Seasonal Intern, Federal Technology Enablement

Career Level Requirement

Early Career

If you are currently pursuing college coursework or have completed a bachelor’s degree or higher in the past 12 months. If it has been more than 12 months since you have graduated from an undergraduate or graduate degree program you should explore experienced career opportunities at KPMG Careers: Experienced Professionals.

KPMG recruits on a rolling basis. Candidates are considered as they apply, until the opportunity is filled. You are encouraged to apply expeditiously to one role for which you are qualified and is of the greatest interest. You are limited to a maximum of two active applications. Give serious thought to your location preference. We strongly recommend applying to the location where you want to build your life and career long-term.

Start Season & Year: Summer 2027

Earliest Graduation Date: May 2026

Latest Graduation Date: Sep 2031

At KPMG we are not only keeping pace with the future of business; we are defining it. Harnessing the full power of AI and digital innovation, we deliver intelligent, data-driven solutions to help our clients navigate change and transform their competitive edge. Our people-first approach makes this possible. KPMG invests in continuous learning by providing the tools and training for you to thrive within a culture that fosters growth and collaboration, whether you're launching your career or bringing decades of experience. Join an inclusive team that inspires excellence, delivers meaningful impact, and empowers you to shape your own future.

KPMG is currently seeking an Advisory Seasonal Intern, Federal Technology Enablement - PhD Data Science/Data Engineer for our Advisory practice.

Responsibilities :

• Conduct research and develop AI-powered capabilities—including anomaly detection models and agentic AI architectures—to solve real-world problems, identify systemic patterns and decompose complex queries into sub-tasks
• Develop reproducible data pipelines to prepare and enrich data and design large-scale multi-relational knowledge graphs to engineer and evaluate complex graph-structured features
• Research and implement post-hoc explainability methods that produce clear rationales for flagged entities, supporting iterative human-in-the-loop refinement for AI models
• Iterate rapidly on models and experimental designs in a fast-paced research environment, while rigorously documenting assumptions and actively managing project risks
• Partner with diverse teams—including data scientists, engineers, policy staff and federal client stakeholders—to translate advanced research outcomes into actionable program integrity recommendations
• Clearly communicate mathematical formulations, algorithmic trade-offs and research findings to both technical collaborators and non-technical business stakeholders using insightful visualizations, reports and presentations

Qualifications :

• Must be enrolled in an accredited college or university and pursuing the following degrees/majors; PhD program in Data Science, Computer Science, Electrical/Computer Engineering, or equivalent program
• Minimum GPA of 3.0 or above
• Applicant must be eligible for or possess a U.S. Government Security clearance
• Strong in technical, business acumen, critical thinking and agility skills; demonstrated ability to excel and drive strategic outcomes, coupled with a professional demeanor and a collaborative leadership approach, in a dynamic, evolving environment; understanding of statistical analysis and machine learning fundamentals; solid foundation in advanced graph or time series techniques preferred
• Strong proficiency in SQL, Python and relevant open-source machine learning and data science libraries
• Exposure to agentic AI frameworks (e.g., LangChain, LangGraph, AutoGen, or similar tool-using agent systems) and familiarity with data visualization tools (Tableau, Power BI) or open-source frameworks (Dash, Plotly, Shiny); experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes) preferred
• Must reside within a commutable distance of the office for this position and be responsible for your own transportation, such as personal vehicle or public transportation, to get to and from office / client locations to satisfactorily fulfill your job duties, as determined by your practice leadership; work location may be in the office, at cl