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 in Computer Science, Data Science, Operations Research, Mathematics, or equivalent.

• Strong desire to grow a career as a Data Scientist in advanced, highly automated industrial manufacturing.

• Have had coworking experience with cross-functional teams such as solution architect, data egngineer, machine learning engineer, etc or equivalent.
  
  Technical Skills:
• Proficiency in Python for data analysis and modeling.

• Solid foundation in statistics and/or machine learning (supervised/unsupervised learning, model evaluation, feature engineering).

• Experience with SQL for data extraction and manipulation.

• Experience with version control (Git).

• Familiarity with building interactive data applications or dashboards (e.g., Streamlit, PowerBI, or similar).

• 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.