This internship is for a student who can develop a yield application/system to improve visibility, speed, and consistency of yield analytics and reporting. You will work with engineering stakeholders to transform manufacturing/test data into usable applications, dashboards, and automation workflows, including data extraction, transformation, visualization, and deployment.
ESSENTIAL ROLES AND RESPONSIBILITES:
- Develop and enhance a Yield Application/System to support engineering yield analytics and decision-making.
- Build data pipelines / ETL to pull and harmonize data from common factory/test sources.
- Work with stakeholders to translate yield analysis needs into system requirements, functional specs, and UI workflows; document assumptions and business rules clearly.
- Create dashboards or reporting layers and ensure metrics definitions are consistent and traceable.
- Implement data quality checks to improve trustworthiness of yield data.
- Support deployment and sustainment activities such as packaging code, organizing repository structure, and preparing for hosting on internal environments.
- Produce user guides / system instructions and handover documentation to enable future enhancement by engineering/automation teams.
- Collaborate with cross-functional partners (engineering, automation, manufacturing/test teams) and participate in reviews/demos of progress and results.
REQUIRED:
- Currently pursuing a Bachelor’s or Master’s degree in Computer Science / Software Engineering / Data Science / Computer Engineering (or related). Computer Science background strongly preferred for automation/system development work.
- Programming capability in Python (or equivalent) with ability to write readable, maintainable code.
- Working knowledge of SQL and relational data concepts.
- Ability to learn and work with manufacturing/test datasets and convert them into actionable outputs.
- Good documentation and communication skills.
PREFERRED:
- Proficient in Dashboarding/BI tools and web application development.
- Excellent in data engineering concepts.
- Familiarity with enterprise deployment environments and tooling.
- Prior project experience demonstrating end-to-end delivery: requirements to build to testing to deploy and finally to documentation.
SKILLS:
- Proficient in Python, SQL, and basic software engineering practices.
- Data manipulation libraries and ability to handle structured datasets efficiently.