Data Scientist Intern

Who We Are

Applied Materials is a global leader in materials science and engineering solutions. Its equipment sits at the foundation of nearly every new semiconductor chip and advanced display made worldwide. The equipment the company builds and services is essential to advancing AI and bringing next-generation chips to market.

The company supports a learning culture where employees grow their careers while taking on challenges and driving innovative solutions for customers. Benefits and wellbeing programs cover work, home, and life beyond.

Team Overview

Join the Supply Chain Analytics team as a Data Scientist Intern. The team uses machine learning, statistical and simulation modeling, generative AI, and actionable insights to improve efficiency across the supply chain.

The team focuses on improving process efficiency through deep analysis, strong visualization, and better UI/UX. Its goal is to strengthen decision support systems and measurement capabilities, producing meaningful gains in key business metrics.

How We Work

AI has changed how quickly a data scientist can work, but it has not changed what makes one good. The team looks for people who use AI tools to move faster while still being able to explain every line of code they ship, defend the assumptions behind a model, and spot an answer that sounds right but is wrong. Strong fundamentals come first, with AI as a multiplier.

Key Responsibilities

• Learn how supply chain operations run, what the underlying data represents, and where decisions are made.
• Perform exploratory data analysis to find trends, opportunities, and actionable insights.
• Write and optimize SQL queries to extract, transform, and analyze data from multiple sources.
• Develop statistical and machine learning models that support supply chain decisions.
• Contribute to GenAI and LLM initiatives, such as AI-powered search, automation, and business applications, including reusable components that can scale.
• Design and improve dashboards and reports using modern tools such as Tableau, Power BI, or custom-built solutions.
• Work with stakeholders to understand business challenges, gather requirements, and find opportunities for improvement.
• Turn business needs into technical requirements and build scalable analytical solutions.
• Communicate findings and recommendations to both technical and non-technical audiences.

Ideal Candidate

The ideal candidate learns new domains quickly, asks questions, and uses feedback to improve their work. The role is both technical and collaborative, since strong technical skills alone will not be enough. Prior supply chain experience is preferred but not required. Curiosity about how a global supply chain works matters most, and the domain will be learned on the job.

This person:

• Examines the data rather than accepting it at face value, and asks the question behind the question.
• Follows a number from the process that produced it, to the table where it lands, to the decision it drives.
• Works with stakeholders to understand which decision the work needs to support.
• Is proactive and takes initiative.
• Has a growth mindset.

Added Advantage

• Interest in supply chain, or a clear goal of building a career in supply chain and operations.
• A supply chain project carried through end to end, from raw data to something people actually used.
• Class or personal projects using LLM applications, such as RAG pipelines, agents, or evaluation harnesses.
• A public portfolio on GitHub, Kaggle, or Tableau Public showing applied projects.