Data Science Intern – Device Characterization (Spring 2027)

First Solar • Perrysburg, OH

11d • Onsite

About The Position

Leading the world’s sustainable energy future, First Solar interns work around the globe/country in research and development, marketing, project development, information technology, engineering, energy services and much more. When you work at First Solar, you will be challenged every day and provided real world experience. You will be given the opportunity to find new solutions and develop new skills to further advance your skillset. At First Solar we strive to foster a culture that encourages innovation through collaboration, urging colleagues to take smart risks, learning from failures and course correcting, and owning personal accountability for contributing to our mission. Expected Length of Internship: 6 months, spanning the Spring/Summer or Summer/Fall semesters (minimum length needed to complete both phases of the project scope below). As an internship company, we allow flexibility to enroll in classes while working as an intern, as long as it doesn’t interfere with the essential functions of the position.

Requirements

• Currently pursuing a Bachelor's or Master's degree in Physics, Electrical Engineering, Materials Science, or Data Science; upper-level undergraduate (junior/senior) or graduate student.
• Proficiency in Python for data analysis (pandas, numpy, scipy).
• Working knowledge of solar cell J-V characteristics (Voc, Jsc, FF, PCE, Rs, Rsh), photoluminescence (PL) or electroluminescence (EL) for semiconductor device characterization, image analysis, curve-fitting or anomaly-detection methods, or basic machine learning.
• Python data analysis (pandas, numpy, scipy, matplotlib).
• Understanding of PV device physics and common J-V curve anomalies (hysteresis, S-kink, shunting).
• Basic image-analysis or statistical-correlation techniques.
• Strong documentation and communication skills to work effectively with an engineer mentor.

Responsibilities

• Build a Python pipeline that auto-extracts Voc, Jsc, FF, PCE, Rs, and Rsh from raw J-V files and classifies anomalies (hysteresis, S-kink, shunting, poor fill factor), delivering the pipeline plus an automated pass/fail report template.
• Extend the pipeline to assess YS300 EL images and correlate image-derived metrics with reverse-scan IV metrics, establishing go/no-go screening criteria for panels entering finishing.
• Present accomplishments and project summary to the leadership team upon completion of the internship.

What This Job Offers

• Career Level: Intern