Manager, Data Science, SMAI

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.