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
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
Preferred Qualifications
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

Work arrangement
No