Summer 2027 - Data Science Intern (R6324)

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

Our Summer Internship program is a wonderful opportunity for Fall and Spring graduates to immerse themselves in an innovative and mission driven culture. Our paid internship program will provide you with hands on mentorship, an opportunity to add real contribution to our company via your internship project, and a possible job offer at the end. You will have the chance to make lifelong friendships, expand your network, and learn all about what it's like to work in a high-growth, fast scaling tech unicorn. You'll take part in company sponsored events and enjoy exploring a new city. Read on below to learn more about this unique internship!

Assess financial and operational data for predictive readiness, identifying useful drivers and material gaps. Build baseline and machine-learning forecasts for a focused Finance use case. Evaluate accuracy, stability, and explainability using appropriate backtesting. Prepare model outputs for Anaplan consumption and demonstrate an integration prototype with the team. Recommend the data, monitoring, and review practices needed to support reliable forecast updates. Present findings and deliver documented, reproducible work that informs the next stage of development.

Pursuing a Bachelor's or Master's degree in data science, statistics, computer science, applied mathematics, economics, or a related field. Coursework or project experience with Python, SQL, statistics, and predictive modeling. Ability to assess data quality and communicate model assumptions and limitations.

Experience with time-series forecasting, feature engineering, or model evaluation. Familiarity with Databricks, financial data, or enterprise planning. Interest in integrating machine learning into practical business workflows. Strong curiosity, collaboration, and problem-solving skills.