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About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1623 open roles
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Listed pay typically runs
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across 1110 roles with salary data.
The Principal Supply Chain Modeling Engineer serves as a core technical architect and individual contributor within the supply chain organization. This role involves personally owning and composing highly complex mathematical models that dictate global hardware procurement strategies. You will write, debug, and scale advanced simulation models from scratch to forecast demand for semiconductor wafers, memory devices, substrates, and long-lead-time sub-assemblies. Key responsibilities include upgrading traditional simulations with Machine Learning and AI models to predict market disruptions and non-linear demand shifts. You must utilize Python, including Pandas, NumPy, and SciPy, along with MATLAB and Excel for data modeling. The role requires translating algorithmic outputs into operational recommendations while auditing massive datasets to ensure precision in multi-variable demand scenarios and long-range sourcing strategies.
Write, debug, and scale advanced simulation models from scratch using Python, MATLAB, and Excel.
Build deterministic and stochastic models to forecast demand for semiconductor wafers, memory devices, and substrates.
Develop and deploy Machine Learning models to predict supply anomalies and non-linear demand shifts.
Audit massive datasets to ensure extreme precision for models that dictate global hardware procurement spend.
Modernize legacy planning sheets into automated, high-performance computing scripts.
Translate complex algorithmic and AI-driven outputs into actionable financial and operational recommendations for the VP.
Stress-test and refine simulation models to future-proof infrastructure with modern AI capabilities.
What we're looking for
Bachelor’s degree in Operations Research, Industrial Engineering, Computer Science, Data Science, Mathematics, Statistics, Physics, or a related quantitative discipline.
15+ years of quantitative experience building, running, and deploying complex mathematical, simulation, or financial models.
Expert proficiency in Python (including Pandas, NumPy, SciPy) and MATLAB.
2+ years of practical, hands-on experience designing, training, and deploying AI or machine learning models.
Ability to build complex, formula-dense spreadsheets, macro automation, and data models for rapid prototyping.
Demonstrated experience presenting raw data and model architectures directly to VP-level leadership.
Advanced degree (Master’s or PhD) in optimization, simulation, or machine learning (preferred).
Prior background in technology hardware, semiconductor, or electronics supply chain sectors (preferred).
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