Manager, Data Science, SMAI
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What you’ll work on
Full postingPartner with Yield, LPD, Cost, IET, Planning leaders to maintain prioritization, risk transparency, and dependency alignment.
From the employer’s posting
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:
What you’ll bring
All qualificationsCore experience
- Understanding of semiconductor fabrication processes, equipment, and device physics is must.
Qualification wording
* 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.
Tools in this posting
- Python
- SQL
- Google Cloud (GCP)
- Plotly
- Streamlit
- Snowflake
Source — Tool mentions in context
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
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:
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. Job Profile(s):
Job description
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- Status in our records
- Active
- First seen by us
- Sep 7, 2026
- Recorded sightings
- 37
- Last seen by us
- Oct 9, 2026
- Employer says posted
- Sep 4, 2026
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