One Thing
Semiconductor Manufacturing
DATASCIENTIST
Neural analysis suggests this role is
optimal for Mid+ candidates.
“DATA SCIENTIST at One Thing. Skills: Data Science, Machine Learning, Data Engineering, Statistical Modeling. Analyze inline/param/probe data to identify top yield detractors. Drive continuous improvement in yield and process optimization”
What You'll Achieve.
Enhance product yield; Improve process variation; Improve process capability; Enhance process capabilities; Enhance process margins
Industry & Context.
Root cause analysis; Problem-solving mindset
What They're Looking For.
Must Have
Bachelor's degree in Computer Science, Data Science, Statistics, AI, or related Engineering field, Minimum 2 years of hands-on experience in data science, analytics, or scripting applications, Willingness to learn semiconductor manufacturing principles, Python programming skills, Working experience with SQL, Familiarity with statistical tools, Familiarity with statistical methodologies (SPC, DOE, FDC/EDA), Familiarity with data-driven problem solving, At least 2 years of working experience applying data visualization tools
Nice to Have
Prior experience or internship in semiconductor industry, electronics manufacturing, or related fields, Basic understanding of semiconductor fabrication processes, equipment, and device physics, Familiarity with advanced analytics for manufacturing and yield applications, Familiarity with automated analysis for manufacturing and yield applications, Knowledge of memory architecture (DRAM/NAND)
What You'll Do.
Analyze inline/param/probe data to identify top yield detractors
Drive continuous improvement in yield and process optimization
Extract datasets from SQL databases
Cleanse datasets from SQL databases
Analyze datasets from SQL databases
Apply data science techniques to solve yield issues
Apply statistical modeling to solve yield issues
Apply machine learning to solve yield issues
Support defect reduction strategies
Assist engineers in running Design of Experiments (DOE)
Assist engineers in analyzing Design of Experiments (DOE)
Enhance process capabilities and margins
Develop automated reports using visualization tools
Develop dashboards using visualization tools
Communicate technical concepts to engineering stakeholders
Communicate project outcomes to engineering stakeholders
How You'll Work.
Team & Collaboration
Semiconductor manufacturing engineering teams; Multi-functional process areas; Process and integration engineers; Data science teams; Semiconductor engineering teams
Communication Scope
Technical concepts; Project outcomes
Full Job Description
**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. **_Job Summary_** As a Data Scientist at Micron, you will employ techniques drawn from mathematics, statistics, and information technology to uncover patterns in data, drive predictive models, and develop actionable solutions for advanced semiconductor manufacturing. Your primary focus will be to support Process Integration and Process Engineering teams to enhance product yield and improve process variation. You will interact closely with multi-functional process areas to solve manufacturing line problems and conduct root cause analysis. In this position, you will help develop software programs, algorithms, and automated processes to cleanse, integrate, and evaluate large datasets from multiple disparate sources—such as inline, param, and probe data—translating them into insights that directly improve process capability and device yield. **_Key Responsibilities_** * 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 solve yield issues and support defect reduction strategies. * Experimentation Support: Assist process and integration engine
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