Advisory Seasonal Intern, Federal Technology Enablement - PhD Data Science/Data Engineer | Multiple Locations Winter 2027
KPMG·New York·United States·Technology & Digital
KPMG is hiring a Advisory Seasonal Intern, Federal Technology Enablement - PhD Data Science/Data Engineer | Multiple Locations Winter 2027 in New York. Posted 2026-09-22; applications close 2026-11-21 (in 59 days).
Role details
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.
Advisory Seasonal Intern, Federal Technology Enablement - PhD Data Science/Data Engineer
KPMG is currently seeking an Advisory Seasonal Intern 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 a PhD program in Data Science, Computer Science, Electrical/Computer Engineering, or an equivalent program.
- Minimum GPA of 3.0 or above.
- Applicant must be eligible for or possess a U.S. Government Security clearance.
- Strong technical acumen, 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 and Python, along with 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 personal transportation to and from office/client locations.
- Work location may be in the office, at client sites, or virtual/remote depending on business need; must be located within the U.S. when working remotely; client site locations may require travel and overnight/extended stay.
- Applicant must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future. KPMG LLP will not sponsor applicants for U.S. work visa status for this opportunity (no sponsorship is available for H-1B, L-1, TN, O-1, E-3, H-1B1, F-1, J-1, OPT, CPT, or any other employment-based visa).
Compensation & Benefits:
- California Salary Range: $86,000 - $100,000.
- KPMG offers a comprehensive compensation and benefits package.
KPMG is an equal opportunity employer and complies with all applicable federal, state, and local laws regarding recruitment and hiring. All qualified applicants are considered for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, citizenship status, disability, protected veteran status, or any other category protected by applicable federal, state, or local laws.
Note: Los Angeles County applicants should note that material job duties for this position are listed above. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties. Pursuant to the California Fair Chance Act and related ordinances, we will consider for employment qualified applicants with arrest and conviction records.
KPMG recruits on a rolling basis. Candidates are encouraged to apply expeditiously to any role(s) for which they are qualified that is also of interest to them.
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Applying to this role
This Advisory Seasonal Intern, Federal Technology Enablement - PhD Data Science/Data Engineer | Multiple Locations Winter 2027 role at KPMG runs through the firm's own careers portal and expects a CV and cover letter written specifically for the posting, not a portable submission carried across firms. Jorb AI's application agent tailors a CV and cover letter from your background to this posting and tracks the role alongside the rest of your applications.
Jorb AI tracks details for Advisory Seasonal Intern, Federal Technology Enablement - PhD Data Science/Data Engineer | Multiple Locations Winter 2027 at KPMG. Postings refresh hourly from primary careers pages. Job details mirror the firm's posting; the apply link goes directly to the source. Last refreshed 2026-09-23.
