Data Scientist 3

Develop machine learning models, statistical algorithms, and data‑driven frameworks to analyze engineering, sensor, metrology, and equipment telemetry data. Conduct advanced image processing and computer vision analysis on wafer, chamber, or process images to derive meaningful features and insights. Apply Bayesian optimization to accelerate engineering workflows, experimental design, and process parameter tuning. Build scalable data pipelines to integrate structured and unstructured data sources. Perform exploratory data analysis, feature engineering, uncertainty quantification, and model validation. Partner with engineers and domain experts to translate physics‑ or chemistry‑based phenomena into analytic or computational models. Deploy and maintain production‑level analytical solutions and monitor their long‑term performance. Visualize results effectively and communicate findings to both technical and non‑technical stakeholders. Use sound statistical design principles (DOE, hypothesis testing, modeling assumptions) to guide experimentation and decision‑making. Contribute to best practices in version control, documentation, reproducibility, and model governance. Ph.D. in Statistics, Computational Physics, Computer Science, or Applied Mathematics. Strong proficiency with Python (NumPy, SciPy, Pandas, scikit‑learn, etc.). Demonstrated experience with advanced image processing (e.g., OpenCV, scikit‑image, deep learning-based methods). Solid foundation in machine learning, statistical inference, and scientific computing. Experience working with large, complex data sets. Strong analytical and problem‑solving skills, particularly in complex technical domains. Ability to collaborate in cross‑functional teams and communicate complex concepts clearly. Experience with physics‑based machine learning and Bayesian statistics is a plus.