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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Conduct routine process capability and performance evaluations to ensure alignment with manufacturing standards.
Identify sources of process variation and yield loss; drive corrective and preventive actions to improve in-line practices, lot-on-hold (LOH) management, and overall process capability.
Execute LOH dispositions and collaborate with treatment teams to accelerate lot movement and minimize production delays.
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BOM Ecosystem Management: Lead the 2nd source strategy in collaboration with Global Procurement, driving multi-million dollar annual savings through material substitution, localization, and technical benchmarking.
Market Intelligence: Conduct deep-dive competitive analysis and market benchmarking to ensure ATSN’s BOM remains the most cost-competitive in the industry.
Scalable Leadership: Manage a diverse team of ~30 Indirect Labor (IDL) and ~20 Direct Labor (DL) professionals, fostering a culture of high performance and technical rigor.
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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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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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Monitor equipment downtime (DT) and develop mitigation plans using MTBF/MTBA analysis; evaluate the effectiveness of scheduled downtime activities and ensure proper verification and buy-off.
Partner with Process Engineering during product development and qualification to ensure molding equipment readiness for high-volume manufacturing (HVM).
Collaborate with cross-functional teams to develop, maintain, and update technical documentation, including control plans, process specifications, SOPs, FMEAs, and technical reports.
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