Senior Manager, Data Science, SMAI

MICRON SEMICONDUCTOR ASIA OPERATIONS PTE. LTD.; Singapore; On-site; SGD 250,000 - 360,000; Full time

Job summary

MICRON SEMICONDUCTOR ASIA OPERATIONS PTE. LTD. seeks an accomplished Manager, Data Science to lead the next evolution of our TD NAND data science and analytics capabilities in Singapore.

This high-impact leadership role intersects semiconductor manufacturing, data science, advanced analytics, and engineering. You will guide teams applying statistical methods, machine learning, and data engineering to solve complex manufacturing challenges and improve process capability, device yield, and

Qualifications

• Master's degree in Computer Science, Electronics Engineering, or a related technical discipline.
• 12+ years of overall professional experience, including 2-3 years leading global Analytics and/or Data Science teams.
• Strong hands-on experience within the semiconductor industry.
• Solid understanding of semiconductor fabrication processes, manufacturing equipment, and device physics.

Responsibilities

• Lead and develop global engineering and analytics teams across multiple levels.
• Drive yield and process optimization with inline, parametric, and probe data.
• Build data pipelines, extract, cleanse, and analyze large datasets from SQL databases and manufacturing sources.
• Advance analytics and machine learning to identify patterns and support defect reduction strategies.
• Enable engineering experimentation with DOE and related analyses to improve process capability.
• Turn data into actionable insights through automated reports, dashboards, and analytics tools (Dash, Plotly, Streamlit).
• Lead cross-functional execution with Yield, LPD, Cost, IET, Planning, and other leaders.

Skills

Leadership

Data science

Machine learning

Statistics

Python

SQL

Data visualization

Education

Master's degree in CS/EE or related technical discipline

Tools

Dash

Plotly

Streamlit

GCP

Snowflake

GitHub

JIRA

Angular

Job description

We are looking for an accomplished Manager, Data Science to lead the next evolution of our TD NAND data science and analytics capabilities in Singapore.

This is a high-impact leadership role at the intersection of semiconductor manufacturing, data science, advanced analytics, and engineering. You will lead teams that apply statistical methods, machine learning, data engineering, and advanced analytics to solve complex manufacturing challenges and improve process capability, device yield, and operational performance.

Working closely with Process Integration, Process Engineering, Inline/Metrology/RDA, Yield, and other cross-functional engineering teams, you will transform large and complex datasets into actionable engineering insights.

You will also play a hands-on role in shaping analytical approaches, developing prototypes, enabling rapid ad-hoc analysis, identifying sources of variation (SOV), and building scalable algorithms and automated data solutions using inline, parametric, probe, sensor, and equipment data.

Because of the highly technical nature of this role, strong semiconductor manufacturing knowledge and experience are essential.

What You'll Do

• Lead & Develop High-Performing Teams
  Lead and develop global engineering and analytics teams across multiple levels. Recruit, mentor, and grow analysts, engineers, and contractors while fostering a culture of technical excellence, innovation, collaboration, and continuous improvement.

• Drive Yield & Process Optimization
  Partner with semiconductor manufacturing and engineering teams to analyze inline, parametric, and probe data, identify key yield detractors, troubleshoot manufacturing issues, and drive continuous improvement.

• Build Data Pipelines & Automation
  Extract, cleanse, integrate, and analyze large datasets from SQL databases, sensor networks, fabrication equipment, and other manufacturing data sources.

• Advance Analytics & Machine Learning
  Apply statistical modeling, machine learning, and data science techniques to identify patterns, understand sources of variation, troubleshoot yield issues, and support defect reduction strategies.

• Enable Engineering Experimentation
  Partner with Process and Integration Engineers to design and analyze Design of Experiments (DOE), improving process capability, margins, and manufacturing performance.

• Turn Data into Actionable Insights
  Develop automated reports, analytical tools, prototypes, and dashboards using technologies such as Dash, Plotly, and Streamlit, making complex engineering insights accessible to key stakeholders.

• Lead Cross-Functional Execution
  Manage a multi-stream analytics and data engineering portfolio while partnering with Yield, LPD, Cost, IET, Planning, and other engineering leaders to align priorities, dependencies, risks, and delivery.

What You Bring

Minimum Qualifications & Experience

• Master's degree in Computer Science, Electronics Engineering, or a related technical discipline.

• 12+ years of overall professional experience, including at least 2-3 years leading global Analytics and/or Data Science teams.

• Strong hands-on experience within the semiconductor industry.

• Solid understanding of semiconductor fabrication processes, manufacturing equipment, and device physics.

Essential Technical Expertise

• Programming & Data Engineering: 8+ years of experience working with Python and strong proficiency in SQL for data extraction, transformation, and analysis.

• Cloud & Data Platforms: Experience with GCP and Snowflake.

• Statistics & Engineering Analytics: 8+ years of experience applying statistical methodologies and data-driven problem solving, including areas such as SPC, DOE, FDC, and EDA.

• Demonstrated ability to translate complex semiconductor manufacturing data into practical engineering insights and solutions.

Highly Desirable Experience

• 8+ years of experience with data visualization and application development technologies such as Dash, Plotly, and Angular.

• Experience with engineering and delivery tools such as GitHub and JIRA.

• Familiarity with AI-assisted development tools such as GitHub Copilot, Code Assist, OpenCode, and Roo Code.

• Proven success delivering multi-stream, cross-functional data engineering and analytics programs.

• Experience using AI to accelerate engineering and software development workflows.

• Experience standardizing modern data platforms and improving data quality, platform performance, and release predictability.

• Strong ability to mentor technical talent and influence technical and architectural direction.

• Excellent stakeholder management and cross-functional communication skills.

• Knowledge of NAND memory architecture is a strong advantage.