Data Analytics Engineer

Job Description:

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.

Responsibilities:

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.