Data Scientist

Job description

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