Data Science Intern - AI & Engineering Analytics (Hybrid, minimum 5 Months)

For more than 75 years, TE Connectivity has partnered with customers to produce highly reliable connectors and sensors that power a connected world. Our innovations enable electric vehicles, aircraft, digital factories, smart homes, medical devices, and next-generation communication infrastructure. The Singapore R&D Center collaborates with CTOs across TE’s Business Units to drive advanced technologies that impact global markets. As part of TE’s Digitalization strategy, we are investing in simulation, modeling, and AI-driven data capabilities to accelerate New Product Development and enable “first-time-right” engineering. We are looking for a Data Science Intern with strong programming skills and interest in Generative AI and Agentic AI, applied to real-world engineering and product design problems. Job Responsibilities: Develop data science and AI solutions to support engineering design, material development, and manufacturing processes Apply machine learning and statistical methods to analyze experimental, simulation, and production data Design and prototype AI/agentic systems to automate engineering workflows, decision support, and knowledge retrieval Work with structured, semi-structured, and unstructured data (including engineering data, images, and text) Perform exploratory data analysis (EDA) to identify patterns, anomalies, and optimization opportunities Develop predictive models (regression, classification, clustering) for engineering applications Collaborate with cross-functional teams including material science, manufacturing, and product engineering Ensure data quality through validation, preprocessing, and robust pipeline development Job Requirements: Required: Currently pursuing a degree in Data Science, Computer Engineering, Computer Science, AI, Statistics, Mathematics, Engineering, or related fields Strong programming skills in Python (NumPy, Pandas, scikit-learn) Understanding of machine learning fundamentals and data analysis techniques Familiarity with engineering data (simulation, experimental, or manufacturing data is a plus) Experience with version control (Git/GitHub/GitLab) Strong analytical thinking and problem-solving skills Good communication and teamwork skills Preferred: Exposure to Generative AI or LLMs, with focus on practical applications Familiarity with agentic AI frameworks (e.g., LangChain, LlamaIndex) or workflow automation Experience with deep learning frameworks (PyTorch, TensorFlow) Knowledge of RAG, embeddings, or data integration techniques Experience in computer vision or engineering-related analytics Understanding of statistical inference, hypothesis testing, and experimental design What You’ll Gain: Hands-on experience applying AI (including agentic systems) to real engineering and product design challenges Exposure to advanced R&D in materials, manufacturing, and digital engineering Mentorship from experienced scientists and engineers Opportunity to contribute to TE’s digital transformation and innovation initiatives