New opportunity

SENIOR/STAFF DATA SCIENTIST, SMART MFG & AI

Micron Technology · Singapore

About this role

The Senior/Staff Data Scientist, Smart Mfg & AI will play a pivotal role in transforming semiconductor manufacturing through data, artificial intelligence, and advanced analytics. The successful candidate will leverage massive volumes of manufacturing, engineering, and business data to develop predictive models, uncover actionable insights, and drive intelligent decision-making across global operations. Responsibilities include collaborating with cross-functional teams to identify opportunities for data-driven solutions, leading analytics and machine learning projects from definition to deployment, and performing exploratory data analysis and root cause investigations. The role involves designing scalable data workflows, partnering with Data Engineers to operationalize models, and mentoring junior team members. Candidates must ensure solutions align with Responsible AI, security, and data governance standards. Required qualifications include a Bachelor's, Master's, or PhD in a quantitative discipline and 5 to 8 years of relevant experience in Data Science, Machine Learning, or Advanced Analytics. Must-have technical skills include proficiency in Python or R, strong SQL, and expertise in statistical modeling and machine learning techniques. Preferred qualifications include experience with distributed computing frameworks (e.g., Apache Spark, Hadoop), data visualization tools like Tableau, and domain knowledge in semiconductor manufacturing or highly automated industrial environments. The role requires strong analytical, problem-solving, and communication skills, with a commitment to continuous learning and the responsible use of AI.

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

Skills for this role

Statistical modelingMachine learningPythonRSQLData preparationFeature engineeringExploratory data analysisModel validationSoftware developmentLarge-scale data processingPySparkSparkRSparklyRApache SparkHadoopHiveHBaseTableauTime-series analysisImage analyticsAnomaly detectionManufacturing Execution Systems (MES)

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

End-to-End Project Lifecycle Management
The role requires leading analytics and machine learning projects from initial problem definition through to deployment and adoption. You must demonstrate the ability to translate complex manufacturing or business challenges into actionable data-driven solutions while ensuring they are successfully integrated into operational workflows.
Scalable Data Engineering and Modeling
You will be responsible for designing and implementing scalable workflows for data preparation, cleansing, and feature engineering. This requires proficiency in large-scale data processing frameworks like Apache Spark and the ability to partner effectively with Data Engineers to ensure models are robust, performant, and ready for production environments.
Cross-Functional Stakeholder Influence
Success depends on collaborating with diverse teams including Engineering, Manufacturing, and Product stakeholders. You must be able to communicate technical findings and complex model recommendations clearly to non-technical audiences to drive business value and ensure organizational alignment.
Responsible AI and Governance Compliance
All solutions must strictly adhere to Micron's standards for Responsible AI, security, privacy, and data governance. You are expected to integrate these compliance requirements into the design phase of every project, ensuring that innovation does not compromise security or ethical standards.

Likely challenges to think through

Navigating Complex Manufacturing Data
Semiconductor manufacturing environments involve massive, high-velocity, and often noisy datasets, including time-series and image data. Prepare by being ready to discuss your experience with anomaly detection and handling datasets with evolving distributions, as well as your approach to root cause investigations in highly automated industrial settings.
Bridging Technical Solutions with Operational Reality
There is a potential trade-off between developing sophisticated machine learning models and ensuring they are practical for Manufacturing Execution Systems (MES). Prepare by focusing on how you balance model complexity with the need for reliability, interpretability, and ease of maintenance in a 24/7 production environment.

Practice your answers

Technical practice

Scalable Data Pipelines in Manufacturing

What to coverDiscuss your approach to designing and implementing scalable data preparation and feature engineering workflows for large-scale manufacturing datasets. Address how you ensure data quality and consistency when integrating disparate sources like Manufacturing Execution Systems (MES).

Handling Evolving Data Distributions

What to coverIn semiconductor manufacturing, data distributions often shift over time. Explain how you detect and mitigate model drift, and what strategies you use to maintain model performance in production environments.

Anomaly Detection Techniques

What to coverDescribe your experience with anomaly detection in highly automated industrial environments. Compare the trade-offs between supervised and unsupervised learning approaches for identifying process deviations in time-series or image data.

Distributed Computing for Large Datasets

What to coverExplain how you leverage distributed computing frameworks like Apache Spark or PySpark to process massive volumes of data. Provide an example of how you optimized a query or a machine learning pipeline for performance in a Hadoop/Hive ecosystem.

Operationalizing Machine Learning Models

What to coverDetail your process for partnering with Data Engineers to move a model from a research environment to a production-ready state. Focus on considerations for scalability, monitoring, and adherence to data governance standards.

Root Cause Investigation via Analytics

What to coverWalk through a scenario where you used exploratory data analysis and statistical modeling to perform a root cause investigation. How did you validate your findings and ensure they were actionable for engineering stakeholders?

Responsible AI and Governance

What to coverHow do you ensure that your machine learning solutions align with Responsible AI, security, and privacy standards? Discuss the importance of model transparency and fairness in the context of high-stakes manufacturing decisions.

SQL and Database Optimization

What to coverBeyond basic queries, how do you optimize complex SQL operations for large-scale data extraction? Suggestion: Prepare to discuss indexing strategies, partitioning, or query plan analysis for performance tuning.

Behavioural practice

Influencing Cross-Functional Stakeholders

What to coverDescribe a time you had to influence non-technical stakeholders to adopt a data-driven solution. How did you translate complex technical findings into business value to gain their buy-in?

Mentoring and Technical Leadership

What to coverProvide an example of how you have mentored junior team members or led a technical initiative. How do you balance your own project ownership with the need to foster growth and best practices within the team?

Managing Conflicting Priorities

What to coverIn a fast-paced environment, how do you manage multiple competing priorities? Share an example of how you assessed the impact of different projects to decide where to focus your efforts.

Continuous Learning and Innovation

What to coverDescribe a time you identified an emerging AI or machine learning technique and applied it to improve an existing process. What was your process for evaluating its feasibility and driving its adoption?

Sharpen your knowledge

Topics to prepare

Semiconductor Manufacturing Data Context
Understand the unique challenges of high-volume, automated manufacturing environments. Exercise: Research common data types in semiconductor fabrication, such as sensor time-series data and yield metrics, and conceptualize how anomaly detection can be applied to detect process deviations.
Responsible AI and Governance
Review frameworks for ethical AI, data privacy, and security compliance. Exercise: Draft a checklist for model deployment that ensures transparency, bias mitigation, and adherence to data governance standards, reflecting the role's requirement to align with corporate AI policies.

Skills to revise

Scalable Data Workflows with Distributed Computing
Practice building data pipelines using Apache Spark or PySpark. Exercise: Create a script that performs feature engineering on a large, multi-gigabyte dataset, focusing on efficient data transformation and cleansing techniques suitable for distributed environments.
Advanced Statistical Modeling and Machine Learning
Demonstrate proficiency in supervised and unsupervised learning techniques. Exercise: Develop a predictive model for a time-series dataset, focusing on model validation strategies and root cause analysis to explain model outcomes to non-technical stakeholders.
SQL for Complex Data Manipulation
Master advanced SQL querying for large-scale data extraction. Exercise: Write complex queries involving window functions, joins, and aggregations to extract actionable insights from a normalized database schema representing manufacturing operational data.

Your preparation checklist

Prepare a project walkthrough focused on industrial data
Select a past project involving large-scale datasets, such as time-series analysis or anomaly detection in an automated environment. Prepare to explain your methodology for data cleaning, feature engineering, and how you operationalized the model for production.
Review semiconductor manufacturing and MES concepts
Familiarize yourself with the role of Manufacturing Execution Systems (MES) and how data science can optimize semiconductor production. Be ready to discuss how you would approach root cause investigations or yield improvement using machine learning.
Demonstrate technical proficiency in distributed computing
Prepare examples of how you have used PySpark, Hadoop, or similar frameworks to handle large-scale data processing. Be ready to explain the trade-offs you made when designing scalable data workflows.
Formulate questions on Responsible AI and governance
Prepare thoughtful questions regarding how Micron implements Responsible AI, data security, and governance in their manufacturing models. This shows alignment with the company's stated commitment to ethical AI practices.
Refine your communication of technical impact
Practice explaining complex machine learning models and their business outcomes to non-technical stakeholders. Focus on how your work directly influenced operational efficiency or decision-making in previous roles.

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

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in Singapore. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…