About the role#
The Predictive Modeling Intern will join the Applied Technology Services (ATS) team to develop statistical models focused on evaluating and predicting component failures and risk. This role involves collaboration with the Failure Analysis and New Technologies teams to work on predictive modeling, data science, and large-scale data analysis using real-world datasets.
What you'll do#
- Assist in collecting, cleaning, and integrating datasets and help build simple data pipelines from internal sources to support analytics and operational insights.
- Support the development, testing, and maintenance of statistical or machine learning workflows by learning team tools and cloud platforms such as Foundry and AWS under the guidance of senior engineers.
- Contribute to dashboards, reports, and data summaries to communicate findings and support engineering and operational decision-making.
What you'll need#
- Currently pursuing a bachelor's, master’s, or PhD degree in Data Science, Electrical, Materials, or Mechanical Engineering, Statistics, or a related field.
- Must be continuing education toward a degree during and/or after the internship.
- Strong foundation in statistics and/or machine learning.
- Proficiency in Python.
- Preferred: Pursuing a Ph.D. in Engineering, Statistics, or Data Science with a power systems emphasis.
Location & details#
- Location: Danville, California.
- Term: Summer 2026.
- Work Modality: On-site.
- Employment Type: Full-time.
- Compensation: $23.08 to $46.46 per hour.
- Note: Visa sponsorship is not available for this position.
