
Senior Manager, Data & Analytics
- San Jose, CA
- Permanent
- Full-time
- Manage, lead, and mentor a team of data engineers, AI/Machine Learning experts, and semiconductor data scientists.
- Define, and execute the technical strategy for AI-driven yield optimization, defect detection, and semiconductor process automation.
- Collaborate with business leaders, chip designers, and manufacturing engineers to drive innovation using data and AI.
- Architect and oversee big data pipelines and real-time analytics frameworks to process semiconductor manufacturing data.
- Develop AI/ML models for predictive maintenance, defect classification, and process optimization. Apply mathematical modeling, machine learning, and optimization techniques to develop AI-driven solutions for semiconductor supply chain management, inventory optimization, and financial forecasting.
- Develop product pricing algorithms and strategies, establishing both immediate and strategic projects to unlock valuable insights from data. Implement advanced statistical and artificial intelligence methods to enable enterprise-wide data-driven decision-making across various domains, including financial processes, pricing, supply chain management, inventory management, engineering systems, human resources, and business risk assessment.
- Demonstrated Expertise (DE) using big data frameworks (Hadoop, Spark, and Databricks), AI/ML platforms (TensorFlow, PyTorch, and Scikit-learn), and cloud technologies (Azure, GCP, or AWS) to drive data-driven insights and innovation in semiconductor analytics;
- Demonstrated knowledge in semiconductor manufacturing, wafer processing, chip design, and embedded computing, enabling optimization of production processes and performance enhancements;
- DE in SQL, NoSQL, Python, R, MATLAB, and real-time streaming technologies like Kafka and Flink to develop scalable data solutions and real-time analytics for semiconductor applications;
- DE leveraging cloud platforms (GCP, Azure, or AWS), Snowflake, DBT, big data tools (Hadoop, Spark, and Kafka), and AI frameworks (TensorFlow, PyTorch, and Scikit-learn) to optimize semiconductor analytics; and
- DE researching and implementing advanced AI/ML methodologies to enhance semiconductor yield, wafer defect analysis, and chip performance prediction.