Advantest Corporation
Data Analytics Engineer
Advantest is seeking a highly skilled and innovative Data Analytics Engineer to lead the design, development, and deployment of advanced analytics solutions that drive data-informed decision-making across semiconductor R&D, test, and operations. You will work closely with engineering, product, and partner teams to build scalable data pipelines, architect machine learning infrastructure, and derive actionable insights from complex, high-volume datasets. This role blends engineering rigor, business strategy, and technical leadership to enable next-gen test solutions across the semiconductor lifecycle. Data Architecture & Engineering
Design and implement high-performance ETL/ELT pipelines and scalable data infrastructure using modern data engineering stacks (e.g., PySpark, Airflow, SQL, AWS). Collaborate with ML/AI teams to deploy robust analytics and ML pipelines (MLOps), ensuring model reproducibility and reliability. Maintain secure and efficient access to data sources across cloud and on-prem environments. Advanced Analytics & Modeling
Lead the development and operationalization of predictive models (e.g., LSTMs for reliability prediction, anomaly detection, SoH estimators) for engineering and business use cases. Guide cross-functional feature engineering efforts, data quality auditing, and domain-specific modeling (e.g., semiconductor fab telemetry, metrology and test data streams). Innovation & Strategy
Identify and prioritize high-value data opportunities aligned with Advantest goals. Integrate state-of-the-art tools including LLMs (e.g., GPT, LangChain, RAG), causal inference frameworks, and neural architecture search (NAS). Drive experimentation pipelines and A/B testing strategies for product development and R&D validation. Project & People Management
Deliver complex projects on time and within scope. Foster a high-performance, inclusive team culture with strong collaboration across hardware, software, and business stakeholders. Establish KPIs to measure impact, data ROI, and performance of analytical solutions. External Collaboration & Advocacy
Engage with academic institutions and industrial partners on joint research, white papers, and benchmark initiatives. Represent Advantest in data science consortiums, standards bodies, and innovation workshops.
Advantest is seeking a highly skilled and innovative Data Analytics Engineer to lead the design, development, and deployment of advanced analytics solutions that drive data-informed decision-making across semiconductor R&D, test, and operations. You will work closely with engineering, product, and partner teams to build scalable data pipelines, architect machine learning infrastructure, and derive actionable insights from complex, high-volume datasets. This role blends engineering rigor, business strategy, and technical leadership to enable next-gen test solutions across the semiconductor lifecycle. Data Architecture & Engineering
Design and implement high-performance ETL/ELT pipelines and scalable data infrastructure using modern data engineering stacks (e.g., PySpark, Airflow, SQL, AWS). Collaborate with ML/AI teams to deploy robust analytics and ML pipelines (MLOps), ensuring model reproducibility and reliability. Maintain secure and efficient access to data sources across cloud and on-prem environments. Advanced Analytics & Modeling
Lead the development and operationalization of predictive models (e.g., LSTMs for reliability prediction, anomaly detection, SoH estimators) for engineering and business use cases. Guide cross-functional feature engineering efforts, data quality auditing, and domain-specific modeling (e.g., semiconductor fab telemetry, metrology and test data streams). Innovation & Strategy
Identify and prioritize high-value data opportunities aligned with Advantest goals. Integrate state-of-the-art tools including LLMs (e.g., GPT, LangChain, RAG), causal inference frameworks, and neural architecture search (NAS). Drive experimentation pipelines and A/B testing strategies for product development and R&D validation. Project & People Management
Deliver complex projects on time and within scope. Foster a high-performance, inclusive team culture with strong collaboration across hardware, software, and business stakeholders. Establish KPIs to measure impact, data ROI, and performance of analytical solutions. External Collaboration & Advocacy
Engage with academic institutions and industrial partners on joint research, white papers, and benchmark initiatives. Represent Advantest in data science consortiums, standards bodies, and innovation workshops.