Responsibilities:
* Design and carry out experiments for SiPh chip performance and characterization.
* Perform and analyze laboratory experiments of chip-level optical systems assembly.
* Develop, manage, and improve setups for photonics and laser device characterization.
* Analyze optical, electrical, and electro-optical measurement data using physics-based models and statistical methods.
* Develop Python-based tools, scripts, and Jupyter notebooks for data analysis, visualization, automation, and reporting.
* Build algorithms for test data processing, performance extraction, trend analysis, and failure/root-cause investigation.
* Support automation of lab equipment, data acquisition, and test flows.
* Apply DOE, statistical analysis, and modeling techniques to improve testing methodologies and product understanding.
* Work with R&D, design, test, and engineering teams to translate experimental results into actionable conclusions.
* Contribute to the development of new testing methodologies, data-analysis methods, and characterization techniques.
* Maintain clear documentation of experiments, analysis flows, code, and results.
* Ensure adherence to experimental protocols and safety procedures.
* Maintain laboratory equipment and supplies.
Skills
* Strong understanding of optics, photonics, and electro-optical systems.
* Hands-on experience with optical laboratory setups and photonics testing.
* Strong experience in physical data analysis, including interpretation of experimental results based on underlying physical mechanisms.
* Experience in preparation and execution of test plans, data analysis, DOE, and technical reporting.
* Strong Python programming skills for data analysis and automation.
* Experience working with tools such as Jupyter Notebook NumPy Pandas SciPy Matplotlib , or similar scientific Python libraries.
* Strong algorithmic thinking and ability to develop robust analysis flows for complex measurement data.
* Good coding practices, including readable code, modular structure, documentation, debugging, and maintainability.
* Experience using Git or other version-control tools.
* Good grasp of statistical analysis methods and tools.
* Ability to analyze large datasets, identify trends, detect anomalies, and extract meaningful performance indicators.
* Willingn










