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Trade Intelligence Analyst Intern - Government Analytics (Summer 2026)

Exiger
United States, Virginia, McLean
1676 International Drive (Show on map)
Jan 10, 2026
Trade Intelligence Analyst Intern - Government Analytics (Summer 2026) Location: McLean, VA (Hybrid) Duration: June 8, 2026 - August 14, 2026 Pay Rate: $25/hour Role Summary Exiger is seeking a highly analytical and mission-oriented Trade Intelligence Analyst Intern to support advanced analytics initiatives within our Government Solutions - Trade & Customs Compliance practice. Reporting to senior government delivery and analytics leaders, this intern will apply data science, trade analytics, and process automation techniques to help identify and prioritize complex trade risk patterns within large-scale shipment data. This is a full-time, paid internship designed for students interested in applying data analytics and AI to real-world trade enforcement and compliance challenges. The role is based in McLean, Virginia, and follows a hybrid work model with close collaboration across distributed government delivery teams. About Exiger Exiger is a leading supply chain risk management (SCRM) software-as-a-service (SaaS) company, using data and AI to help organizations intelligently identify risk across global trade and supply chain ecosystems. Exiger's mission is to make the world a safer and more transparent place to succeed. In 2025, Exiger was named a Leader in the Gartner Magic Quadrant for Supplier Risk Management Solutionsand a Value Leader in the Spend Matters Fall 2025 Solution Map. Within Exiger, our Government Solutions teams deliver advanced analytics and AI-driven insights that support trade compliance, enforcement, and national security missions. Key Responsibilities As a Trade Intelligence Analyst Intern, you will contribute to the development and refinement of analytics that support trade risk identification and prioritization: Apply data analytics and trade compliance logic to large, multi-source shipment datasets Support the development of composite targeting and ranking methodologies that integrate shipment-level attributes, shipper behavior signals, and inferred data Assist in refining and evaluating risk scoring frameworks, ensuring outputs are defensible, reproducible, and operationally usable Analyze ranking shifts between analytical cycles and document the drivers behind changes Support evaluation of HS code confidence and inference, assessing how data availability impacts signal strength and prioritization Aggregate and analyze shipper behavior patterns to identify trade intermediaries and routing archetypes Identify data gaps and quality issues and quantify their impact on analytical outcomes Contribute to process documentation and analyst enablement materials, ensuring explainability and transparency Collaborate with analysts and engineers to align outputs to real-world enforcement and prioritization workflows Example focus areas may include: Shipment targeting and prioritization frameworks HS code confidence and inference analysis Shipper behavior profiling and pattern detection Data quality assessment and validation checks Analyst-facing documentation and workflow alignment Qualifications and Required Experience Currently pursuing a BS or MS in Computer Science, Data Analytics/Data Science, AI/ML, or a related field Strong programming skills in Python (data wrangling, validation, ML workflows) and SQL (joins, window functions, performance fundamentals) Experience with core data concepts: schema design, ETL, testing, version control, and reproducibility Understanding of modern analytics and AI practices, including feature engineering, model evaluation, and explainability Strong written communication skills with the ability to translate technical findings into clear, decision-focused narratives Comfortable working with complex, incomplete, and sensitive datasets in structured environments Preferred (Not Required): Familiarity with trade compliance, logistics, supply chain, or sustainment data concepts Experience with entity resolution, fuzzy matching, or record linkage techniques Exposure to graph analytics and relationship-based risk analysis Hands-on experience with data quality frameworks (e.g., Great Expectations or similar patterns) Exposure to compliance-driven or secure data environments Why Intern with Us: Valuable hands-on experience in a dynamic and supportive team Mentorship from industry experts Hybrid work environment with meaningful in-office collaboration Hands-on experience working with our Government Delivery team Dedicated mentorship and professional development opportunities Exposure to real-world SaaS security, compliance, and risk management challenges Inclusive, mission-driven culture focused on impact, learning, and growth Networking Opportunities within the organization #Li-hybrid #Li-DNI This is a hybrid position based in McLean, VA. The pay rate is $25.00 /hour. Exiger is revolutionizing the way corporations, government agencies and banks manage risk and compliance with a combination of technology-enabled and SaaS solutions. In recognition of the growing volume and complexity of data and regulation, Exiger is committed to creating a more sustainable risk and compliance environment through its holistic and innovative approach to problem solving. Exiger's mission to make the world a safer place to do business drives its award-winning AI technology platform, DDIQ, built to anticipate the market's most pressing needs related to evolving ESG, cyber, financial crime, third-party and supply chain risk. Exiger has won 30+ AI, RegTech and Supply Chain partner awards. Exiger's core values are courage, excellence, expertise, innovation, integrity, teamwork and trust. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law. Exiger's hybrid work policy is periodically reviewed and adjusted to align with evolving business needs.