2026 Summer Data Scientist Intern- Master's (Santa Clara, CA)

Who We Are

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips - the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world - like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world.

What We Offer

Location:
Santa Clara,CA

You'll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible-while learning every day in a supportive leading global company. Visit our Careers website to learn more.

At Applied Materials, we care about the health and wellbeing of our employees. We're committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits.

TEAM OVERVIEW:

Join our dynamic Supply Chain Analytics team as a Data Scientist Intern! Our mission is to revolutionize efficiency by harnessing the power of machine learning, statistical and simulation modeling, and actionable insights.

We are dedicated to transforming process efficiency through deep analysis, cutting-edge visualization, and enhanced UI/UX. Our goal is to elevate decision support systems and measurement capabilities, driving substantial improvements in key business metrics. Be a part of our journey to make a significant impact!

KEY RESPONSIBILITIES:

• Perform exploratory data analysis on large datasets.
• Design and improve dashboards and reports in Tableau /Power BI to visualize metrics and trends, driving data-backed decisions.
• Build and implement advanced statistical and machine learning models using Python (Pandas, NumPy, SciPy, and scikit-learn), with a focus on improving supply chain processes.
• Write/optimize SQL to extract and join data from multiple sources.
• Work on GenAI and LLM projects, including fine-tuning models, creating embedding-based search systems, and developing AI-driven prototypes to enhance business operations.
• Develop, train, and validate machine learning models (e.g., clustering, similarity models, time-series forecasting, anomaly detection, recommendation systems) to solve complex supply chain challenges.
• Support model implementation by packaging code, running tests, and helping with deployment steps.

• In-depth understanding of supply chain management principles and practices.
• Strong SQL skills (joins, CTEs, window functions, performance basics)
• Strong Python skills for data work (Pandas, NumPy, SciPy, scikit-learn)
• Basic understanding of machine learning concepts and model evaluation (train/test, cross-validation, precision/recall, RMSE, bias/variance)
• Familiarity with big data tools such as Databricks and Apache Spark (PySpark preferred)
• Experience with data visualization tools (Tableau Desktop or Power BI)
• Exposure to GenAI / LLMs and modern ML workflows (examples: prompt design, embeddings, model selection, basic fine-tuning concepts, evaluation)
• Advanced working knowledge of Microsoft Excel.

• Currently pursuing a master's degree in computer science, data science, or another quantitative field.
• GPA 3.0 or above preferred

• Experience in supply chain topics (planning, inventory, logistics, procurement, reverse value chain)
• Experience building end-to-end ML projects (data prep -> model -> evaluation -> deployment)
• Experience with recommendation systems, similarity matching.
• Experience with Databricks ETL, ingestion pipelines.
• Public portfolio (GitHub, Kaggle, Tableau Public) showing applied projects.
• Relevant certifications (optional)