AI Data Scientist Technical Leader (Hybrid)

What you'll do

• Develop and deploy models for yield analysis, failure prediction, and forecasting yield and quality metrics.
• Build AI agents to automate root-cause analysis and yield-improvement workflows; analyze manufacturing, test, and parametric datasets.
• Partner with engineering teams and communicate findings through visualizations, reports, and presentations.

What they ask for

• MS or PhD in Computer Science, Electrical Engineering, Data Science, Statistics, or a related field.
• 8+ years of experience in data science, machine learning, or data analytics.
• Proficiency in Python and data science libraries; experience building, validating, and deploying models for classification, regression, anomaly detection, and time-series forecasting.
• Hands-on experience with AI agent frameworks; understanding of semiconductor manufacturing, test data, and yield analysis.

Nice to have

• Experience with cloud data platforms and big data technologies; familiarity with Optuna or other hyperparameter optimization frameworks.
• Knowledge of hardware debug, root-cause analysis, and reliability engineering.

Pay

$186,900.00 to $267,700.00 (salary range; base salary not specified); $186,900.00 - $307,800.00 (salary range; base salary not specified); $166,300.00 - $274,100.00 (salary range; base salary not specified)

Tools

Python, pandas, scikit-learn, XGBoost, TensorFlow, PyTorch, matplotlib, seaborn

AI focus

Applies AI/ML to semiconductor yield analysis, early failure prediction, forecasting, and automated root-cause analysis using AI agents.