Job Description
Advantest is seeking a versatile Senior Data Analytics Engineer to design, develop, and deploy data solutions that bridge infrastructure, analytics, and machine learning. In this individual contributor role, you will own the full lifecycle of data projects—from building scalable pipelines to developing predictive models—that empower semiconductor R&D, test, and operations teams. Due to the cross-functional nature of our team, you’ll collaborate closely with engineers, data scientists, and business stakeholders to deliver end-to-end solutions, leveraging both data engineering and ML/AI expertise.
This role is ideal for a hands-on engineer who thrives in a fast-paced environment and is comfortable wearing multiple hats, from architecting data workflows to modeling complex datasets and integrating ML models into production.
Key Responsibilities
Data Infrastructure & Pipeline Development
· Design and optimize ETL/ELT pipelines to process large-scale, high-velocity semiconductor data (e.g., fab telemetry, test results).
· Build and maintain scalable data platforms using modern tools across cloud and on-prem environments.
· Ensure data quality, security, and accessibility for downstream analytics and ML use cases.
ML & Advanced Analytics Integration
· Partner with data scientists to operationalize predictive models (e.g., reliability prediction, anomaly detection, classification) into production pipelines.
· Develop and maintain ML infrastructure (MLOps) for model monitoring, retraining, and versioning.
· Perform feature engineering, statistical analysis, and domain-specific modeling (e.g., time-series analysis for semiconductor manufacturing).
Cross-Functional Problem-Solving
· Collaborate with semiconductor engineers to translate domain challenges into data-driven solutions.
· Experiment with emerging tools (e.g., LLMs, causal inference) to innovate on analytics capabilities while balancing business impact.
· Communicate technical findings to non-technical stakeholders through dashboards or strategic recommendations.
Requirements
· Education: M.S./Ph. D in Computer Science, Data Science, Engineering, or a quantitative field.
· Experience: 5+ years of hands-on experience in at least two of the following areas:
o Data engineering (ETL/ELT, pipeline development, cloud platforms).
o ML engineering (MLOps, model deployment, production ML systems).
o Advanced analytics (predictive modeling, statistical analysis, domain-specific problem-solving).
· Technical Skills:
o Must-have:
- Python, ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn), Pandas/Dask/Polars, and NumPy.
- API Development (FastAPI).
- Experience with ML deployment (e.g., Docker, Kubernetes, etc.), and Linux.
- Dashboarding tools (e.g., Grafana, Power BI, Dash, Tableau).
- DevOps tools (e.g., git, GitHub, Jenkins).
o Preferred:
- Knowledge of semiconductor manufacturing and testing processes.
- Experience with LLM-based tools and agentic solutions (e.g., prompt engineering, workflow automation, decision support systems).
- Experience with causal inference techniques (e.g., identifying root causes of manufacturing defects, optimizing process parameters).
- Cloud platforms (AWS/Azure/Google Cloud) and cloud orchestration tools (e.g., Terraform).
Job Type: Full-time
Pay: $129,650.00 - $200,400.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
Application Question(s):
- Will you now or in the future require sponsorship for employment visa status?
Work Location: In person
