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