Data Analytics Intern

BorgWarner • Auburn Hills, MI

7h • Onsite

About The Position

BorgWarner is a global product leader in delivering innovative and sustainable mobility solutions for the vehicle market. We are a company of innovators and independent thinkers that brings together talented employees, meaningful work, and amazing technology in a unique environment. At BorgWarner we constantly work towards our vision of a clean and energy-efficient world.

We are seeking a highly motivated Data Analytics Intern to develop an AI-agent–based system that supports technology scouting and project selection in the automotive electrification domain. The intern will build a prototype platform that ingests technical literature, benchmark data, and product requirements, and synthesizes structured insights to inform future engineering projects. This role sits at the intersection of AI, data engineering, and automotive systems .

Requirements

• Current full-time enrollment in an accredited college, university, vocational/trade school.
• Ability to report onsite at least three days to our Auburn Hills Campus
• Pursuing a BS/MS/PhD in Computer Science, Data Science, Electrical/Computer Engineering, or related field.
• Coursework or experience in machine learning and natural language processing (e.g., transformers, prompt engineering, fine-tuning).
• Proficiency with Python (pandas/NumPy) and at least one ML framework (PyTorch or TensorFlow).
• Familiarity with vector databases/embeddings (FAISS, Milvus, pgvector, or Azure Cognitive Search) and RAG concepts.
• Experience building APIs/services (FastAPI/Flask) and working with Git.
• Ability to analyze large datasets, design experiments, and communicate results clearly.

Responsibilities

• Develop pipelines to ingest and process technical papers (IEEE, SAE, patents), benchmark datasets (CSV, Excel, PDF, structured reports) and internal product requirements.
• Implement role-based AI agents, including paper summarization and relevance scoring agents, customer signal extraction agents, benchmark comparison, and gap-analysis agents by using large language models (LLM’s) with tool usage, retrieval, and memory.
• Design and implement a knowledge storage layer including vector database for search, structured data tables for benchmark and KPI’s.
• Develop automated summaries highlighting emerging technology trends, gaps between customer requirements and existing solutions and potential project opportunities.
• Implement human-in-the-loop review workflows and document system architecture, agent roles, and usage instructions.

What This Job Offers

• Career Level: Intern
• Education Level: No Education Listed