MICRON SEMICONDUCTOR ASIA OPERATIONS PTE. LTD. Hiring! Full Time Data Scientist in North Region (Singapore), Earn up to SGD 4,100 - Ricebowl

Our vision is to transform how the world uses information to enrich life for all.
Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

As a Data Scientist (Smart Manufacturing & AI) at Micron, you will develop data-driven solutions that improve semiconductor manufacturing yield, quality, and productivity. Working alongside senior data scientists and product owners, you will build predictive models, develop AI-powered solutions, and contribute to analytical pipelines that solve real-world manufacturing challenges involving die-level, wafer-level, and test data.

Responsibilities and Tasks

• Test Solutions Engineering (TSE) 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 TSE 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.

  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.​

Preferred Qualifications

• Exposure to LLMs, agentic AI frameworks, or RAG pipelines (internship experience, academic or personal projects welcome).

• Experience with time-series data, process/manufacturing data, or handling concept drift/imbalanced datasets.

• Familiarity with cloud platforms (Google Cloud Platform, AWS, or Azure) and data visualization tools (Tableau, Power BI).

• Exposure to ETL tools, containerization (Docker), or web frameworks (Angular, React, FastAPI).

• Coursework or projects involving optimization, mathematical programming, or statistical modeling.