01Overview
Job Requirements
About the RoleWe 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 ResponsibilitiesDesign, develop, and deploy machine learning models for equipment failure prediction and fault classificationAnalyze 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 interpretabilityCollaborate with process engineers, equipment engineers, and failure analysis teamsSelect, justify, and evaluate appropriate ML algorithmsValidate models using metrics such as precision/recall, F1-score, ROC-AUC, and early failure detection accuracyDocument models, assumptions, and results for technical and cross-functional stakeholdersMentor junior data scientists and contribute to best practices
Work Experience
Required Qualifications5+ years of professional experience as a Data Scientist or Machine Learning EngineerStrong proficiency in Python (Pandas, NumPy, scikit-learn)Proven experience with time-series data modelingHands-on experience building classification and predictive modelsExperience with failure prediction, reliability analytics, or equipment health monitoringWorking knowledge of fault tree analysis (FTA) or structured root-cause analysisStrong feature engineering skills for noisy, real-world industrial dataAbility to clearly communicate technical results to engineering stakeholders
Preferred QualificationsExperience in semiconductor manufacturing or equipment systems (etch, deposition, lithography, inspection, metrology)Familiarity with process data, tool logs, alarms, and sensor telemetryExperience with survival analysis, RUL estimation, or anomaly detectionExposure to model explainability techniques (e.g., SHAP, feature importance)Experience deploying models into production or factory systemsBackground in reliability engineering, systems engineering, or failure analysisWhat Success Looks LikeAccurate and reliable failure prediction models with low false-positive ratesClear linkage between data-driven predictions and physical failure mechanismsMeasurable improvements in tool uptime, yield, and maintenance planningStrong collaboration with cross-functional engineering teamsRepresentative Tech StackPython (Pandas, NumPy, scikit-learn)Time-series analysis librariesMachine learning frameworksVisualization and reporting tools .
