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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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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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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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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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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Lead post‑Fab process development, optimization, and sustainment to ensure manufacturability, robustness, yield, and cost competitiveness.
Apply AI‑assisted data analysis, statistical learning, and automation tools to identify process trends, anomalies, and improvement opportunities in high‑volume manufacturing environments.
Partner with Fab, Assembly, Test, Quality, Equipment, IT, and external suppliers to deploy data‑driven and AI‑enabled solutions that enhance process capability, cycle time, and operational efficiency.
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Support production line coordinator to pursue the fulfillment of all production goals.
Have clear understanding about different kinds of raw/auxiliary material/tools and know how to use them correctly to support high efficiency/cost effective manufacturing.
Implement TPM and other activities which is helpful to line continuous improvement.
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Support production line coordinator to pursue the fulfillment of all production goals.
Have clear understanding about different kinds of raw/auxiliary material/tools and know how to use them correctly to support high efficiency/cost effective manufacturing.
Implement TPM and other activities which is helpful to line continuous improvement.
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