Process Optimization: Use regression and reinforcement learning to suggest optimal machine parameters (e.g., bond force, temperature profiles) for new product introductions (NPI).
Data Engineering: Build and manage data pipelines that ingest high-frequency sensor data from packaging equipment (e.g., Die Attach, Wire Bonders) via SECS/GEM or MQTT protocols.A/B Testing & Monitoring: Design experiments to validate model performance on the shop floor and monitor for "model drift" as machine parts wear down over time.
Core ML: 2+ years of experience with Python (Scikit-Learn, XGBoost, Pandas) and Deep Learning frameworks (PyTorch or TensorFlow).
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Process Optimization: Use regression and reinforcement learning to suggest optimal machine parameters (e.g., bond force, temperature profiles) for new product introductions (NPI).
Data Engineering: Build and manage data pipelines that ingest high-frequency sensor data from packaging equipment (e.g., Die Attach, Wire Bonders) via SECS/GEM or MQTT protocols.A/B Testing & Monitoring: Design experiments to validate model performance on the shop floor and monitor for "model drift" as machine parts wear down over time.
Core ML: 2+ years of experience with Python (Scikit-Learn, XGBoost, Pandas) and Deep Learning frameworks (PyTorch or TensorFlow).
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Production Deployment: Transition models from local environments (Jupyter/Python) into the factory’s execution system using Docker and REST APIs .
Process Optimization: Use regression and reinforcement learning to suggest optimal machine parameters (e.g., bond force, temperature profiles) for new product introductions (NPI).
Data Engineering: Build and manage data pipelines that ingest high-frequency sensor data from packaging equipment (e.g., Die Attach, Wire Bonders) via SECS/GEM or MQTT protocols. A/B Testing & Monitoring: Design experiments to validate model performance on the shop floor and monitor for "model drift" as machine parts wear down over time. Required Technical Skills
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