Manager, Data Science, SMAI

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing.

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: Experience & Leadership 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. 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. 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. 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. About Micron Technology, Inc. We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life   for all . With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands.