Manager, Data Science, SMAI

MICRON SEMICONDUCTOR ASIA OPERATIONS PTE. LTD.; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

MICRON SEMICONDUCTOR ASIA OPERATIONS PTE. LTD. in Singapore seeks a seasoned Analytics Leader to guide global data science and analytics teams.

You will drive yield optimization, data engineering, and advanced modeling to support semiconductor manufacturing excellence. You will mentor engineers, coordinate cross-functional projects, and deliver dashboards and automated reports to stakeholders. A strong background in Python, SQL, and cloud platforms is essential, with prior semiconductor

Qualifications

• Masters in Computer Science or Electronics Engineering.
• 12+ years overall experience with at least 2-3 years' experience in leading global Analytics and/or Data Science teams.

Responsibilities

• Lead and develop global analytics and engineering teams across multiple levels.
• Drive yield and process optimization with manufacturing teams.
• Design and manage data pipelines from SQL databases, sensor networks, and tool logs.
• Apply advanced analytics and ML to troubleshoot yield issues and defects.
• Support DOE experiments for process capability and margins.
• Develop automated dashboards/reports using Dash/Plotly to communicate results to stakeholders.
• Coordinate cross-functional governance and prioritization across streams.

Skills

Team leadership

Analytics leadership

Python

SQL

GCP

Snowflake

Dash

Plotly

Jira

DOE/Statistics

Education

Master's degree in Computer Science or Electronics Engineering

Tools

Code Assist

OpenCode

RooCode

Github Copilot

GitHub

JIRA

Job description

What You\'ll Do

• Team Leadership & Talent Development: Lead and develop global engineering and analytics teams across multiple levels. Recruit, mentor, and grow analysts, engineers, and contractors into high-performing contributors. Foster a culture of technical excellence, collaboration, and continuous improvement.
• Yield & Process Optimization: Collaborate with semiconductor manufacturing engineering teams to analyze inline/param/probe data to identify top yield detractors and drive continuous improvement.
• Data Pipeline & Automation: Extract, cleanse, and analyze datasets from SQL databases, sensor networks, and fabrication tool logs to support semiconductor manufacturing operations.
• Advanced Analytics & Modeling: Apply data science techniques, statistical modeling, and machine learning to troubleshoot yield issues and support defect reduction strategies.
• Experimentation Support: Assist process and integration engineers in running and analyzing Design of Experiments (DOE) to enhance process capabilities and margins.
• Visualization & Communication: Develop automated reports and dashboards using visualization tools (e.g., Dash, Plotly, streamlit) to communicate technical concepts and project outcomes effectively to engineering stakeholders.
• Cross-Functional Execution & Governance: Manage a multi-stream delivery portfolio with predictable, high-quality releases. Partner with Yield, LPD, Cost, IET, Planning leaders to maintain prioritization, risk transparency, and dependency alignment.

What You Bring:

Minimum Required Qualifications/Experience

• Masters in Computer Science, Electronics Engineering
• Prior experience in the semiconductor industry is must. Understanding of semiconductor fabrication processes, equipment, and device physics is must.
• Minimum 12+ years overall experience with at least 2-3 years' experience in leading global Analytics and/or Data Science teams.

Must have Technical Skills

• Programming & Data Engineering: Minimum 8 years of experience in Python programming skills and experience with SQL for data extraction and manipulation. Cloud & Data Platforms: GCP Suite, Snowflake
• Statistical Analysis: Minimum 8 years of expertise in role which is with statistical tools, methodologies (such as SPC, DOE, or FDC/EDA), and data-driven problem solving.

Highly Desirable/Preferred skills or experience

• Data Visualization: At least 8 years of working experience utilizing data visualization tools (e.g., Dash, Plotly, Angular) to present complex engineering data clearly.
• Engineering & Delivery: Experience in Github, JIRA will be plus.
• Expertise in Code Gen tools: Code Assist, Open Code, Roo Code, Github copilot will be important.
• Proven success delivering multi-stream, cross-functional data engineering programs.
• Experience with AI-driven engineering acceleration and modern data-stack standardization.
• Strong track record improving data quality, release predictability, and platform performance.
• Ability to mentor technical talent and influence architectural direction.
• Excellent stakeholder engagement and cross-functional communication skills.
• Knowledge of memory architecture (NAND) is added advantage.