Job Requirements
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.
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Design, develop, and deploy machine learning models for equipment failure prediction and fault classification
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Analyze time-series data from semiconductor tools (sensor telemetry, logs, process traces)
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Perform advanced feature engineering (lags, rolling windows, trends, seasonality, event-based features)
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Apply fault tree analysis (FTA) concepts to support root-cause analysis and improve model interpretability
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Collaborate with process engineers, equipment engineers, and failure analysis teams
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Select, justify, and evaluate appropriate ML algorithms
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Validate models using metrics such as precision/recall, F1-score, ROC-AUC, and early failure detection accuracy
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Document models, assumptions, and results for technical and cross-functional stakeholders
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Mentor junior data scientists and contribute to best practices
Work Experience
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5+ years of professional experience as a Data Scientist or Machine Learning Engineer
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Strong proficiency in Python (Pandas, NumPy, scikit-learn)
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Proven experience with time-series data modeling
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Hands-on experience building classification and predictive models
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Experience with failure prediction, reliability analytics, or equipment health monitoring
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Working knowledge of fault tree analysis (FTA) or structured root-cause analysis
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Strong feature engineering skills for noisy, real-world industrial data
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Ability to clearly communicate technical results to engineering stakeholders
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Experience in semiconductor manufacturing or equipment systems (etch, deposition, lithography, inspection, metrology)
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Familiarity with process data, tool logs, alarms, and sensor telemetry
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Experience with survival analysis, RUL estimation, or anomaly detection
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Exposure to model explainability techniques (e.g., SHAP, feature importance)
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Experience deploying models into production or factory systems
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Background in reliability engineering, systems engineering, or failure analysis
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Accurate and reliable failure prediction models with low false-positive rates
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Clear linkage between data-driven predictions and physical failure mechanisms
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Measurable improvements in tool uptime, yield, and maintenance planning
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Strong collaboration with cross-functional engineering teams
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Python (Pandas, NumPy, scikit-learn)
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Time-series analysis libraries
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Machine learning frameworks
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Visualization and reporting tools
