Data Scientist

Job Requirements About the Role We are seeking a Data Scientist with 5+ years of experience to develop machine learning solutions for failure prediction, classification, and fault analysis in semiconductor manufacturing and equipment systems. This role focuses on time-series modeling, equipment health monitoring, and root-cause analysis using structured reliability methods such as fault tree analysis (FTA). You will work with complex, high-volume data from semiconductor tools (sensor signals, logs, process data) to improve tool uptime, yield, and operational reliability. Key Responsibilities Design, develop, and deploy machine learning models for equipment failure prediction and fault classification Analyze time-series data from semiconductor tools (sensor telemetry, logs, process traces) Perform advanced feature engineering (lags, rolling windows, trends, seasonality, event-based features) Apply fault tree analysis (FTA) concepts to support root-cause analysis and improve model interpretability Collaborate with process engineers, equipment engineers, and failure analysis teams Select, justify, and evaluate appropriate ML algorithms Validate models using metrics such as precision/recall, F1-score, ROC-AUC, and early failure detection accuracy Document models, assumptions, and results for technical and cross-functional stakeholders Mentor junior data scientists and contribute to best practices Work Experience 5+ years of professional experience as a Data Scientist or Machine Learning Engineer Strong proficiency in Python (Pandas, NumPy, scikit-learn) Proven experience with time-series data modeling Hands-on experience building classification and predictive models Experience with failure prediction, reliability analytics, or equipment health monitoring Working knowledge of fault tree analysis (FTA) or structured root-cause analysis Strong feature engineering skills for noisy, real-world industrial data Ability to clearly communicate technical results to engineering stakeholders Representative Tech Stack Python (Pandas, NumPy, scikit-learn) Time-series analysis libraries Machine learning frameworks Visualization and reporting tools