Senior 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 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.​ Required Qualifications & Skills Education / Experience • Bachelor's degree in Computer Science, Data Science, Mathematics, Operations Research, Engineering, or a related quantitative/technical discipline. • Experience developing data science, machine learning, or AI solutions for complex, data-intensive business problems. • Experience with end-to-end machine learning development, including data preparation, feature engineering, model development, evaluation, optimization, and production integration. • Experience working with multidisciplinary teams or integrating machine learning solutions into broader data, decision, or production systems. Technical Skills • Strong proficiency in Python for data analysis, machine learning, and model development. • Strong foundation in machine learning, including supervised learning, feature engineering, model evaluation, representation learning, and model optimization. • Experience using SQL and working with large-scale structured datasets. • Experience developing scalable data and feature-engineering pipelines for machine learning applications. • Experience deploying or integrating machine learning models into production systems and automated decision workflows. • Familiarity with modern AI/ML techniques such as gradient boosting, graph neural networks, representation learning, Generative AI, RAG, or LLM-based systems • Strong verbal and written communication skills, with the ability to explain complex analytical results clearly. • Ability to apply baseline digital fluency and role‑appropriate AI literacy to use AI‑enabled tools responsibly and effectively for research, analysis, content creation, problem‑solving, operational tasks, and achieving business outcomes.​ Preferred Qualifications • Exposure to LLMs, agentic AI, or RAG-based solutions, through work experience, internships, academic work, or personal projects. • Experience working with time-series, manufacturing/process data, imbalanced datasets, or concept drift. • Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform. • Exposure to ETL/data pipelines, Docker, APIs, or web application frameworks. • Coursework, research, or projects involving optimization, mathematical programming, or advanced statistical modeling.