Job description

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. Requirements: • M.S. or Ph.D. in Computer Science, Data Science, or a related field. • 5+ years of experience in data analytics or data engineering, including leadership roles. • Proven experience building and deploying ML/AI systems (e.g., LSTM, GRU, CNN, XGBoost) in production environments. • Hands-on expertise with modern big data tools and infrastructure: AWS, Docker, Kubernetes, PySpark, Airflow, SQL, Git. • Solid understanding of statistical methods, time series forecasting, anomaly detection, and causal inference techniques. • Strong communication skills with experience translating technical outcomes into business strategy. • Tools & Technologies: • Languages: Python, SQL, C++, R, Java • Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, FastAPI - Data Platforms: AWS, Azure, Snowflake, Hadoop, Spark - Visualization: Tableau, PowerBI, Matplotlib, Seaborn • DevOps: Docker, Kubernetes, GitHub Actions, MLFlow Additional Skills Preferred (but not required): • Experience in semiconductor or high-tech manufacturing domains. • Familiarity with LangChain, vector search, and multimodal model deployment (audio, image, text). • Prior experience in startup or innovation lab settings, driving end-to-end productization of research insights. • Knowledge of IP/data governance, model interpretability (e.g., SHAP, LIME), and performance benchmarking

Requirements