Data Science Intern: GenAI & Supply Chain Analytics

Applied Materials, Inc.; Austin (TX); Presencial; USD 34.000 - 48.000; A tiempo parcial

Descripción de la vacante

Applied Materials, Inc. in Austin seeks a Data Scientist Intern to learn the business and apply ML, statistics, and analytics to optimize supply chain decisions. You will build models, perform data analysis, and develop dashboards using Tableau or Power BI while collaborating with stakeholders across functions.

The ideal candidate is pursuing a quantitative degree with strong SQL, Python, and communication skills, and has an interest in GenAI and AI-driven analytics.

Formación

• Pursuing a bachelor's degree in a quantitative field with GPA 3.0+ preferred.
• Foundational knowledge of databases, SQL, and data modeling.
• Strong Python skills for data analysis and ML basics.

Responsabilidades

• Learn the business and data landscape of supply chain operations.
• Perform exploratory data analysis to find trends and insights.
• Write and optimize SQL queries from multiple sources.
• Develop statistical and ML models for supply chain decisions.
• Contribute to GenAI and LLM initiatives, including AI-powered search.
• Design and enhance dashboards/reports using Tableau, Power BI, or custom tools.
• Collaborate with stakeholders to gather requirements and identify improvement opportunities.
• Translate business needs into scalable analytical solutions.
• Communicate findings to technical and non-technical audiences.

Educación

Bachelor's degree in Data Science, Computer Science, Engineering, Statistics, Operations Research, or related

Herramientas

Tableau

Power BI

Databricks

Snowflake

BigQuery

Git

Bitbucket

ETL/ELT

Tableau Public

Descripción del empleo

Applied Materials, Inc. in Austin seeks a Data Scientist Intern to learn the business and apply ML, statistics, and analytics to optimize supply chain decisions. You will build models, perform data analysis, and develop dashboards using Tableau or Power BI while collaborating with stakeholders across functions.

The ideal candidate is pursuing a quantitative degree with strong SQL, Python, and communication skills, and has an interest in GenAI and AI-driven analytics.