New opportunity

Data Scientist

Micron Technology · Singapore

About this role

Join Micron's Smart Manufacturing and AI journey as a Data Scientist to transform massive volumes of manufacturing and business data into predictive insights and intelligent solutions. You will tackle complex challenges in a sophisticated semiconductor manufacturing environment, partnering with cross-functional teams to drive operational excellence and innovation. Responsibilities include developing predictive models, performing exploratory data analysis, and deploying AI and agentic AI solutions. You will extract and validate data from diverse sources, design analytical approaches for business problems, and translate findings into actionable recommendations. Required qualifications include a Bachelor's degree in a quantitative discipline, experience in statistical modeling, feature engineering, and machine learning (supervised, unsupervised, semi-supervised), proficiency in SQL, and experience with large datasets and AI/agentic AI development. Must have strong software development, analytical, and communication skills. Preferred qualifications include a Master's degree or PhD, experience with PySpark, GCP, Snowflake, TensorFlow, ETL processes, and visualization tools like Tableau or Power BI. Experience with time-series data, image analytics, or semiconductor manufacturing environments is highly desirable. The role requires collaboration across teams and a commitment to Micron's core values of innovation, tenacity, and customer focus.

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

Skills for this role

PythonRSQLMachine LearningStatistical ModelingFeature EngineeringAgentic AIPySparkSparkRSparklyRGoogle Cloud PlatformSnowflakeTensorFlowSSISETLJavaScriptAngularTableauPower BITime-series analysisImage analyticsManufacturing Execution Systems

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

Predictive Modeling for Manufacturing
The core of this role is developing and deploying predictive models to improve yield, quality, and operational efficiency within a semiconductor manufacturing environment. You must demonstrate the ability to translate complex manufacturing data into actionable insights that directly impact business outcomes.
Data Engineering and Pipeline Management
You are responsible for the end-to-end data lifecycle, including extracting, integrating, cleansing, and validating data from diverse structured and unstructured sources. Proficiency in SQL, ETL processes, and handling large-scale datasets is essential for building reliable analytical foundations.
Cross-Functional Collaboration
Success requires partnering with Data Engineers, Manufacturing Engineers, and UX teams. You must be able to communicate technical findings to both technical and non-technical stakeholders to drive adoption of AI-driven solutions across the organization.
AI and Agentic AI Development
Beyond traditional machine learning, the role specifically requires the development and deployment of agentic AI solutions. You must stay current with emerging technologies and apply them responsibly to automate processes and solve complex manufacturing problems.

Likely challenges to think through

Data Complexity and Variability
Semiconductor manufacturing involves large, complex datasets with frequently changing distributions. Prepare by highlighting your experience with outlier detection, missing data handling, and adapting models to non-stationary data environments.
Translating Technical Insights to Business Value
The difficulty lies in bridging the gap between raw data analysis and operational improvements. Prepare by practicing how you explain the 'why' behind your models and how your recommendations specifically drive efficiency or cost savings in a production setting.
Integration with Legacy Manufacturing Systems
Working with Manufacturing Execution Systems (MES) requires understanding the constraints of industrial environments. Prepare by discussing how you approach software development and model deployment in systems where uptime and process stability are critical.

Practice your answers

Technical practice

Predictive Modeling in Manufacturing

What to coverExplain your approach to developing predictive models for semiconductor manufacturing datasets. Focus on how you handle high-dimensional data, feature engineering for process parameters, and ensuring model robustness against frequently changing distributions.

Agentic AI Implementation

What to coverDescribe your experience in developing and deploying agentic AI solutions. Discuss the architecture you would use to enable autonomous decision-making in a manufacturing environment and how you ensure these agents operate reliably and safely.

Handling Large-Scale Data Pipelines

What to coverDetail your process for extracting, integrating, and cleansing data from diverse structured and unstructured sources. Address how you utilize tools like PySpark or SQL to manage large datasets and ensure data quality for downstream modeling.

Machine Learning Frameworks and Deployment

What to coverDiscuss your experience with TensorFlow or similar frameworks in a production environment. Explain your strategy for model deployment, monitoring, and maintaining performance over time in a complex industrial setting.

Time-Series and Image Analytics

What to coverHow do you approach feature engineering for time-series sensor data or image analytics in a manufacturing context? Provide examples of how these specific data types inform predictive maintenance or quality control.

Statistical Modeling and Validation

What to coverExplain your methodology for validating machine learning models, particularly when dealing with semi-supervised learning scenarios. How do you ensure your analytical approach effectively solves the underlying business problem?

Database Querying and Optimization

What to coverDescribe your expertise in SQL and working with enterprise platforms like Snowflake. Explain how you optimize complex queries to handle large-scale manufacturing data efficiently.

Data Visualization for Stakeholders

What to coverHow do you translate complex analytical findings into actionable recommendations using tools like Tableau or Power BI? Suggest how you tailor your visualizations for both technical and non-technical manufacturing stakeholders.

Behavioural practice

Cross-Functional Collaboration

What to coverDescribe a situation where you had to partner with non-data roles, such as Manufacturing Engineers or UX teams, to solve a technical problem. Explain how you aligned on goals and communicated your findings to drive operational efficiency.

Managing Multiple Priorities

What to coverProvide an example of how you manage competing priorities when working on multiple data science projects. Discuss your process for balancing immediate operational needs with long-term innovation initiatives.

Learning Agility and Innovation

What to coverShare an example of how you have stayed current with emerging AI or data science technologies and applied that knowledge to improve a process. Highlight your intellectual curiosity and how you evaluate new tools for adoption.

Problem Solving with Tenacity

What to coverTell me about a time you faced a significant technical challenge or a complex data issue that required persistence to resolve. Focus on the actions you took to overcome the obstacle and the final impact on the project.

Sharpen your knowledge

Topics to prepare

Agentic AI Architectures
Review the conceptual framework of agentic AI, focusing on autonomous decision-making loops, tool-use capabilities, and multi-agent orchestration. This is directly relevant to the role's requirement for developing and deploying agentic AI solutions within a manufacturing context.
Semiconductor Manufacturing Data Dynamics
Study the unique challenges of semiconductor data, specifically high-frequency time-series sensor data, image analytics for defect detection, and datasets with frequently changing distributions. This is critical for understanding the operational environment described in the job posting.
Statistical Modeling for Process Optimization
Refresh knowledge on statistical techniques for outlier detection, missing data handling, and process control. These are essential for the role's focus on driving operational excellence and yield improvement in a manufacturing setting.

Skills to revise

Advanced SQL and ETL Pipeline Design
Practice writing complex SQL queries for data extraction and designing robust ETL processes to integrate structured and unstructured data. The role requires extracting and validating data from diverse sources to support predictive modeling.
Distributed Computing with PySpark
Develop proficiency in processing large-scale datasets using PySpark. Given the requirement to handle massive volumes of manufacturing data, this skill is highly desirable for building scalable machine learning applications.
Machine Learning Frameworks (TensorFlow)
Build a project using TensorFlow to implement a supervised or semi-supervised learning model. This aligns with the preferred qualifications for experience with machine learning frameworks to solve complex manufacturing problems.

Your preparation checklist

Prepare a project walkthrough focused on manufacturing or time-series data
Develop a concise presentation of a past project where you applied machine learning to large-scale, complex datasets. Emphasize your process for feature engineering, handling missing data, and detecting outliers, as these are critical for semiconductor manufacturing environments.
Demonstrate proficiency in agentic AI and scalable ML frameworks
Be prepared to discuss your experience with agentic AI development and how you have utilized frameworks like TensorFlow or PySpark to build scalable solutions. Highlight specific instances where you deployed models into production environments.
Review SQL and ETL best practices for enterprise databases
Brush up on advanced SQL querying and ETL pipeline design, specifically for platforms like Snowflake or GCP. Be ready to explain how you extract, integrate, and validate data from diverse, unstructured sources.
Formulate questions regarding Micron's Smart Manufacturing and AI initiatives
Prepare thoughtful questions about how the data science team collaborates with Manufacturing Engineers and UX teams to drive operational excellence. Ask about the current challenges in managing datasets with frequently changing distributions in their specific manufacturing context.
Align your communication style with Micron's core values
Prepare to provide examples of how you have demonstrated innovation, tenacity, and collaboration in cross-functional projects. Practice translating complex technical findings into actionable business recommendations for non-technical stakeholders.

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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