Data Scientist

Responsibilities and Tasks Product Quality (PQ) Predictive Solutions: Build and productionize machine learning models (classification and regression) to support predictive and prescriptive analytics across semiconductor test and manufacturing processes. Agentic AI and LLM Development: Contribute to the design and implementation of multi-agent systems for PQ workflows, including code generation, automated troubleshooting, and process optimization. Work with retrieval-augmented generation (RAG) pipelines: identify data sources, develop retrieval strategies, and improve context quality for LLM-based applications.

Assist in implementing tool-using capabilities, function calling, and agent memory systems.\ Software Engineering and Productionalization: Develop maintainable code and analytical pipelines suitable for high-volume manufacturing environments. Optimize for performance, latency, and token efficiency. Collaborate with senior team members on testing, deployment, and monitoring.

Communicate analytical insights, model behavior, and results clearly to technical and non-technical stakeholders Integrates AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness,exercising sound judgment and complying with organizational standards and legal requirements.​ Contributes to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one’s scope of work.​ Integrates AI-assisted tools and insights into da