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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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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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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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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Analyze failing wafers with cross‑functional teams to identify root causes and implement corrective actions, including close collaboration with suppliers and customers.
Support manufacturing in achieving best‑in‑class cycle time, productivity, and On‑Time Delivery (OTD).
Lead and participate in cross‑functional (local/global) teams on new technology development, process transfers, and new product introductions.
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