This role pays more than
95%
of similar roles. Most pay
$155,900–$210,875
— the shaded band above.
At the midpoint, this role pays about
$310k
versus about
$183k
for comparable roles.
Based on 240 similar postings.
Employer
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
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
As a Principal Supply Chain Modeling Engineer, you will serve as a core technical architect and individual contributor focused on supply network simulation. You will personally develop, debug, and scale complex mathematical models to dictate global hardware procurement strategies for components like semiconductor wafers, memory devices, and substrates. Your daily work involves building deterministic and stochastic models to forecast demand while upgrading legacy systems with Machine Learning and AI capabilities to predict market disruptions. You will utilize Python, including Pandas, NumPy, and SciPy libraries, along with MATLAB and Excel to create high-performance computing scripts. The role requires translating algorithmic outputs into actionable financial recommendations for executive leadership. You will manage data integrity across massive datasets to ensure precision in multi-variable demand scenarios while collaborating with procurement, finance, and engineering teams on long-range sourcing strategies.
Develop and scale complex mathematical models in Python and MATLAB to dictate global hardware procurement strategy.
Simulate and forecast demand for critical components like semiconductor wafers, memory devices, and long-lead sub-assemblies.
Integrate Machine Learning and AI models into existing simulations to predict market disruptions and non-linear demand shifts.
Audit massive datasets to ensure extreme precision for high-stakes financial and operational decision-making.
Convert legacy planning tools into automated, high-performance computing scripts through continuous optimization.
Translate complex algorithmic outputs into actionable strategic recommendations for executive leadership.
Perform stress-testing on multi-variable demand scenarios to inform long-range sourcing strategies.
What we're looking for
Bachelor's degree in a quantitative engineering discipline such as Operations Research, Industrial Engineering, Computer Science, Data Science, Mathematics, Statistics, or Physics.
15+ years of experience building, running, and deploying complex mathematical, simulation, or financial models.
2+ years of practical, hands-on experience designing, training, and deploying AI or algorithms based on learning from data.
Expert proficiency in Python (including Pandas, NumPy, SciPy) and MATLAB is non-negotiable.
Ability to build complex, formula-dense spreadsheets with macro automation for rapid prototyping.
Experience presenting raw data and model architectures directly to executive leadership.
Advanced degree in optimization, simulation, or machine learning is preferred.
Prior experience in the technology hardware, semiconductor, or electronics supply chain sectors is preferred.
Manufacturing Test
NPI
PLM
DFM
DFT
DFR
Reliability Engineering
Data Analytics
Test Automation
Industry 4.0
Root Cause Analysis
Statistical Analysis
Configuration Control