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# Graphene Photonics Data Analyst

- **Company:** Black Semiconductor
- **Location:** Aachen, Germany
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Data Infrastructure, Data Systems, Data Visualization, Python (Programming Language), Statistical Process Control (SPC)
- **Published:** August 28, 2026
- **Apply:** https://www.careerjet.de/jobad/debb81ffea118f8cf3ebbf69fc682677b7

## About the Role

An M.S. or PhD in Data Science, Physics, or another relevant technical discipline
At least two years of relevant industry or research experience; a related PhD project
Strong Python skills and a genuine passion for building high-quality software
A proven track record of designing, developing, and owning a software pipeline or technical project end-to-end
A solid foundation in device physics, including solid-state physics, semiconductor devices, and photonics
Experience with statistical analysis, model fitting, and parameter extraction
Experience with data visualization and tracking trends across wafers, lots, or manufacturing processes
The ability to work with laboratory or fab instrument interfaces

## Description

As a Graphene Photonics Data Analyst, you will transform complex characterization data into reliable insights that guide device design, process development, and manufacturing decisions. You will own and evolve our analysis software and data infrastructure, develop new methods for emerging experiments, and help scale our analytical capabilities as we transition from R&D to high-volume production.
To join our team, you should be excited to:
Own and continuously improve our characterization analysis software suite
Develop new analysis methods as experiments and measurement capabilities evolve
Build and maintain reliable, scalable data infrastructure
Ensure the integrity, accuracy, and traceability of analysis outputs
Turn raw measurement data into actionable insights for process and design decisions
Extract device parameters from measured data and benchmark results against physical and compact models
Perform statistical process and yield analyses to identify trends, correlations, and outliers across wafers and lots
Correlate characterization results with process parameters, closing the loop between measurement and process control
Integrate analysis outputs with fab MES and data systems to enable real-time process feedback
Define standardized analysis protocols and transition mature R&D code into robust, production-ready tools
Optimize analysis pipelines for throughput and scalability as we move toward high-volume production


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