Data Science Intern – Device Characterization (Spring 2027)

UEC; Perrysburg (OH); On-site; USD 30,000 - 34,000; Full time

Job summary

UEC is seeking an intern to build data pipelines that automate device characterization for perovskite solar panels. You will work with an engineer mentor using historical IV data, image/performance libraries, and computing resources.

You will develop a Python pipeline to auto-extract Voc, Jsc, FF, PCE, Rs, and Rsh from raw J-V files and classify anomalies, delivering the pipeline plus an automated pass/fail report.

Qualifications

• Pursuing a Bachelor's or Master's in physics, electrical engineering, materials science, or data science.
• Strong Python data-analysis skills (pandas, numpy, scipy, matplotlib).
• Familiarity with PV J-V characteristics and EL/PL is a plus.
• Experience with image analysis or basic ML is desirable.
• Excellent documentation and communication skills.

Responsibilities

• Build data pipelines to automate device characterization for perovskite panels.
• Collaborate with an engineer mentor using historical IV data, image libraries, and computing resources.
• Develop a Python pipeline to auto-extract Voc, Jsc, FF, PCE, Rs, and Rsh from raw J-V files and classify anomalies.
• Deliver the pipeline along with an automated pass/fail report template.
• Extend the pipeline to assess EL images and correlate image-derived metrics with IV metrics.
• Establish go/no-go screening criteria for panels entering finishing to standardize anomaly classification.
• Present accomplishments and project summary to leadership at internship end.

Skills

Python programming

Data analysis

Image analysis

Anomaly detection

Documentation & communication

Education

Bachelor's or Master's in Physics / Electrical Engineering / Materials Science / Data Science

Tools

Pandas

NumPy

SciPy

Matplotlib

Job description

Your Role

Here's what you will be doing:

• Build data pipelines to automate device characterization for perovskite panels.
• Work alongside an engineer mentor using historical IV data, image/performance libraries, and computing resources.
• Develop a Python pipeline (Months 1-3) to auto-extract Voc, Jsc, FF, PCE, Rs, and Rsh from raw J-V files and classify anomalies such as hysteresis, S-kink, shunting, and poor fill factor.
• Deliver the pipeline along with an automated pass/fail report template.
• Extend the pipeline (Months 4-6) to assess YS300 EL images and correlate image-derived metrics with reverse-scan IV metrics.
• Establish go/no-go screening criteria for panels entering finishing to standardize anomaly classification and reduce BOM losses.
• Present accomplishments and project summary to the leadership team upon internship completion.

About You

The company is looking for candidates with:

• Current pursuit of 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, including pandas, numpy, scipy, and matplotlib.
• Working knowledge of solar cell J-V characteristics (Voc, Jsc, FF, PCE, Rs, Rsh), photoluminescence (PL) or electroluminescence (EL) for semiconductor device characterization.
• Experience with image analysis, curve-fitting, anomaly-detection methods, or basic machine learning.
• Understanding of PV device physics and common J-V curve anomalies such as hysteresis, S-kink, and shunting.
• Basic image-analysis or statistical-correlation techniques.
• Strong documentation and communication skills to collaborate effectively with an engineer mentor.

Compensation & Benefits

• Estimated salary range: $22-$25 per hour.