New opportunity

Staff Data Scientist, SMAI

Micron Technology · Singapore

About this role

The Staff Data Scientist, SMAI will design, develop, and program methods, processes, and systems to consolidate and analyze unstructured, diverse big data sources to generate actionable insights and solutions for client services and product enhancement. Responsibilities: Interact with product and service teams to identify questions and issues for data analysis and experiments. Develop and code software programs, algorithms, simulation and optimization models, and automated processes to cleanse, integrate, and evaluate large datasets from multiple disparate sources. Identify meaningful insights from large data and metadata sources. Interpret and communicate findings to product, service, and business managers. Explore and apply new frontier ideas to enable breakthrough solutions. Work with management and global manufacturing sites to identify optimization opportunities and deliver solutions to improve production metrics. Drive global collaboration, alignment, and benchmarking of optimization and simulation solutions in planning and operations. Leverage AI, LLMs, and AI-assisted development tools to accelerate analytics, software development, simulation modeling, and solution delivery. Apply Generative AI and Agentic AI concepts to enhance productivity, automate repetitive engineering activities, improve decision support, and enable scalable digital transformation. Evaluate emerging AI technologies for incorporation into planning, operations optimization, and digital twin solutions. Required Qualifications: Bachelor's degree in Computer Science, Data Science, Industrial Engineering, Operations Research or equivalent. 5+ years of strong software engineering experience with Python and SQL. Working experience in semiconductor or manufacturing operations simulation modeling using discrete event simulation. Working experience in optimization modeling and Operations Research, including Mixed Integer Programming (MIP), Linear Programming (LP), Constraint Programming, and related techniques. Knowledge of cloud solutions, API, GIT, and ETL. Strong problem-solving, structured logic, and communication skills. Experience leveraging AI-assisted tools (e.g., Microsoft Copilot, GenieLLM) and familiarity with Generative AI, LLMs, prompt engineering, and AI agents. Preferred Qualifications: Experience developing Digital Twin solutions for semiconductor manufacturing, supply chain, capacity planning, scheduling, or operations optimization. Experience with Java and other programming languages.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonSQLJavaData ScienceOperations ResearchOptimization ModelingMixed Integer ProgrammingLinear ProgrammingConstraint ProgrammingDiscrete Event SimulationDigital TwinCloud SolutionsAPIGITETLGenerative AILarge Language Models (LLMs)Prompt EngineeringAI AgentsAI-assisted development tools

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Optimization and Simulation Modeling
The role requires designing and programming complex models using Operations Research techniques like MIP, LP, and Constraint Programming, alongside discrete event simulation. You must demonstrate the ability to apply these to semiconductor manufacturing, capacity planning, and supply chain operations to improve production metrics.
AI-Driven Productivity and Digital Transformation
You are expected to leverage Generative AI, LLMs, and AI-assisted development tools to accelerate analytics and software delivery. This includes evaluating emerging AI technologies to build scalable digital twin solutions and automating repetitive engineering tasks.
Cross-Functional Collaboration and Communication
You must interact with product, service, and business managers to translate complex data findings into actionable insights. Driving global alignment and benchmarking of solutions across international manufacturing sites is a core expectation for this staff-level position.

Likely challenges to think through

Integrating Disparate Manufacturing Data
Consolidating unstructured, diverse big data from global manufacturing sites presents a significant technical hurdle. Prepare to discuss your experience with ETL pipelines, cloud solutions, and data cleansing strategies for large-scale, noisy industrial datasets.
Balancing Innovation with Operational Stability
Implementing 'new frontier' AI and Agentic AI concepts in a high-stakes semiconductor manufacturing environment requires balancing breakthrough innovation with the need for reliable, production-grade systems. Be prepared to explain how you validate AI-driven models before deployment to ensure they do not disrupt critical operations.

Practice your answers

Technical practice

Optimization Modeling Techniques

What to coverDiscuss your experience applying Mixed Integer Programming (MIP), Linear Programming (LP), and Constraint Programming to solve complex manufacturing or supply chain problems. Be prepared to explain how you select the appropriate technique based on problem constraints and computational complexity.

Discrete Event Simulation in Manufacturing

What to coverDescribe a scenario where you used discrete event simulation to model semiconductor or manufacturing operations. Focus on how you validated the model against real-world data and used it to identify production bottlenecks or capacity improvements.

Digital Twin Development

What to coverExplain your approach to building and maintaining a Digital Twin for operations optimization. Address how you integrate disparate data sources, maintain synchronization with physical assets, and ensure the model provides actionable insights for decision-makers.

Leveraging AI-Assisted Development

What to coverProvide examples of how you have used tools like Microsoft Copilot or GenieLLM to accelerate your software development or data science workflows. Discuss the trade-offs between productivity gains and the need for rigorous code review and validation.

Generative AI and Agentic AI Implementation

What to coverExplain how you would apply Agentic AI concepts to automate repetitive engineering tasks or improve decision support systems. Discuss the challenges of reliability and governance when deploying autonomous agents in a production environment.

Data Integration and ETL Pipelines

What to coverDetail your experience in cleansing and integrating large, unstructured datasets from multiple disparate sources. Suggestion: Focus on your ability to build robust, scalable ETL pipelines that handle the high-volume, high-velocity data typical of semiconductor manufacturing.

Cloud Solutions and Scalability

What to coverDiscuss your experience deploying optimization models or simulation solutions in a cloud environment. Address considerations for scalability, cost management, and API design when exposing these models to other business units.

Python and SQL Proficiency

What to coverDescribe how you have utilized Python and SQL to solve complex data engineering or analytical problems in your previous roles. Highlight your ability to write efficient, maintainable code that integrates with larger production systems.

Behavioural practice

Communicating Technical Insights to Stakeholders

What to coverDescribe a time when you had to explain complex optimization or simulation findings to non-technical business managers. Use the STAR method to detail how you translated technical data into actionable business recommendations.

Cross-Functional Collaboration

What to coverGive an example of a project where you collaborated with global manufacturing sites or different departments to drive alignment on an optimization solution. Explain how you managed conflicting priorities and ensured the final solution met global production metrics.

Problem Solving Under Uncertainty

What to coverShare a situation where you had to identify a solution for a production issue using incomplete or unstructured data. Explain your structured logic approach and how you validated your findings before implementation.

Adaptability to New Technologies

What to coverProvide an example of a time you had to rapidly learn and apply a new technology or framework to meet a project deadline. Describe the steps you took to master the tool and how you successfully integrated it into your workflow.

Sharpen your knowledge

Topics to prepare

Operations Research and Optimization Modeling
The role requires expertise in Mixed Integer Programming (MIP), Linear Programming (LP), and Constraint Programming for manufacturing optimization. Exercise: Review your experience applying these techniques to solve resource allocation or scheduling problems. Be prepared to discuss how you formulate objective functions and constraints for complex semiconductor or supply chain scenarios.
Discrete Event Simulation (DES) for Manufacturing
The position emphasizes using DES to model semiconductor operations. Exercise: Reflect on past projects where you built simulation models to evaluate system performance. Prepare to explain how you validated your models against real-world manufacturing metrics and how you used them to drive decision-making.
Generative AI and Agentic AI Integration
The role involves applying GenAI and AI agents to automate engineering workflows. Exercise: Research current frameworks for building AI agents and consider how they could be applied to automate repetitive data analysis or simulation tasks. Prepare to discuss the trade-offs between different LLM architectures for industrial applications.

Skills to revise

Advanced Python and SQL for Data Engineering
The role requires 5+ years of software engineering experience. Exercise: Practice writing efficient, production-grade Python code for data pipelines and complex SQL queries for large, disparate datasets. Focus on code modularity, performance optimization, and version control using GIT.
AI-Assisted Development Workflow
The job requires leveraging tools like Microsoft Copilot or GenieLLM to accelerate development. Exercise: Document specific instances where you used AI coding assistants to improve your productivity in software development or data analysis. Be ready to explain how you maintain code quality and security when using these tools.
Digital Twin Development
Developing Digital Twin solutions is a preferred qualification. Exercise: Review your experience in creating virtual representations of physical systems. Prepare to discuss how you integrate real-time data, simulation models, and optimization algorithms to create actionable digital twins for capacity planning or operations.

Your preparation checklist

Prepare a portfolio of Operations Research and Simulation projects
Document specific examples where you applied Mixed Integer Programming (MIP), Linear Programming (LP), or Constraint Programming to solve complex scheduling or capacity planning problems. Highlight your experience with discrete event simulation in a manufacturing context.
Demonstrate your AI-assisted development workflow
Prepare a narrative on how you have integrated tools like Microsoft Copilot or similar coding assistants into your daily software development lifecycle. Be ready to explain how you use these tools to accelerate analytics, simulation modeling, or code generation while maintaining accuracy.
Develop a case study on Digital Twin implementation
Create a structured walkthrough of a Digital Twin project you led or contributed to. Focus on how you consolidated disparate data sources, the specific optimization models used, and the measurable impact on production metrics or operational efficiency.
Refine your communication of technical insights to business stakeholders
Prepare to explain a complex technical solution—such as an optimization algorithm or an AI-driven process improvement—to a non-technical audience. Focus on how you translate data findings into actionable business decisions for product or service managers.
Formulate questions regarding Micron's AI and Digital Twin roadmap
Prepare thoughtful questions about how the SMAI team currently evaluates emerging AI technologies for manufacturing operations. Ask about the specific challenges in scaling digital transformation solutions across global manufacturing sites.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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