Graduate Intern, Artificial Intelligence & Data Science - Summer 2027

About the Role

HD Supply is seeking a Graduate Intern, Artificial Intelligence & Data Science to support strategic initiatives that leverage advanced analytics, machine learning, and artificial intelligence to drive business performance and innovation across the enterprise. As one of the largest industrial distributors in North America, HD Supply relies on data-driven decision-making to optimize customer experiences, supply chain operations, inventory management, pricing, forecasting, and enterprise processes. This internship provides an opportunity for graduate students to apply advanced analytical and technical skills to real-world business challenges while working alongside experienced data scientists, AI practitioners, engineers, and business leaders. The Graduate AI & Data Science Intern will contribute to projects involving predictive analytics, machine learning models, generative AI, intelligent automation, large language models (LLMs), agentic AI, and decision-support solutions. Interns will gain exposure to enterprise-scale datasets, cloud-based analytical environments, and the practical application of AI technologies within a large, complex organization. This internship is designed for graduate-level talent seeking hands-on experience translating advanced analytics and AI concepts into measurable business outcomes.

Key Responsibilities

Analyze large and complex datasets to identify trends, opportunities, and business insights. Support the development, testing, and refinement of machine learning and AI models. Assist in data preparation, feature engineering, model evaluation, and performance monitoring. Develop visualizations, dashboards, reports, and presentations to communicate findings. Partner with business stakeholders to understand challenges and translate them into analytical solutions. Contribute to Generative AI and Agentic AI initiatives focused on automation and productivity. Document methodologies, assumptions, findings, and recommendations. Pre