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

Required Qualifications

• 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

• Experience in semiconductor manufacturing or equipment systems (etch, deposition, lithography, inspection, metrology)
• Familiarity with process data, tool logs, alarms, and sensor telemetry
• Experience with survival analysis, RUL estimation, or anomaly detection
• Exposure to model explainability techniques (e.g., SHAP, feature importance)
• Experience deploying models into production or factory systems
• Background in reliability engineering, systems engineering, or failure analysis

What Success Looks Like

• Accurate and reliable failure prediction models with low false-positive rates
• Clear linkage between data-driven predictions and physical failure mechanisms
• Measurable improvements in tool uptime, yield, and maintenance planning
• Strong collaboration with cross-functional engineering teams

Representative Tech Stack

• Python (Pandas, NumPy, scikit-learn)
• Time-series analysis libraries
• Machine learning frameworks
• Visualization and reporting tools