About the role#
The Process Engineering – Data Analytics group partners with manufacturing plants and central engineering to build analytics that improve yield, stability, and cost. As an intern, you will contribute to advanced process data analytics projects, including statistical process control (SPC) modernization, anomaly detection, and model-in-the-loop improvements. You will design, build, and evaluate data solutions that transform raw manufacturing data into actionable insights.
What you'll do#
- Data engineering & preparation: Ingest, clean, and join sensor and mesoscale datasets in Databricks; implement validation and data quality checks.
- Statistical process analysis: Apply SPC, capability studies, and time-series methods to detect drift and instability; communicate findings to process owners.
- Visualization & reporting: Build clear notebooks and dashboards for operators and engineers; document assumptions, data lineage, and model limits.
- Collaboration: Work with process engineers, manufacturing subject matter experts, and digital transformation teams to prioritize backlogs and translate requirements into analytics deliverables.
What you'll need#
- Currently pursuing a BS/MS in Data Science, Industrial/Process/Manufacturing Engineering, Computer Science, Statistics, or a related field.
- Coursework or projects in probability, statistics, data structures, and introductory machine learning.
- Practical experience with Python and SQL; familiarity with version control (Git) and collaborative workflows.
- Availability for the full 10-week program from May 27, 2026, to July 31, 2026.
Location & details#
- Location: Austin, Texas.
- Modality: On-site.
- Term: Summer 2026 (10 weeks).
- Employment type: Full-time, paid internship.
- Note: This position does not support immigration sponsorship.
