Sobre esta vaga de Principal Supply Chain Modeling Engineer na NVIDIA
NVIDIA is seeking an elite, hands-on Principal Engineer who specializes in supply network simulation. This person will serve as the core technical architect and strategic partner to our Vice President. In this critical individual contributor role, you will not manage a team. You will personally own and compose highly complex mathematical models that dictate our global hardware procurement strategy.
Your models will directly simulate and forecast our demand for fundamental technology elements, including semiconductor wafers, memory devices, substrates, and key sub-assemblies that require lengthy procurement periods. You must be an absolute modeling purist who thrives on extreme detail and possesses deep technical coding skills. Your role includes future-proofing our infrastructure by improving deterministic systems with modern AI capabilities. Does this sound like your dream job? If passionate about supply chain and working at the forefront of the AI revolution, come show us what you got!
What you’ll be doing:
End-to-End Code Execution: Personally write, debug, and scale advanced simulation models in Python, MATLAB, and Excel from scratch.
AI Augmentation: Upgrade traditional simulations by building and deploying Machine Learning (ML) and AI models to predict supply anomalies, market disruptions, and non-linear demand shifts.
Material Forecasting: Build deterministic and stochastic models to simulate multi-variable demand scenarios for silicon wafers, memory, substrates, and critical long-lead-time sub-assemblies.
Executive Partnership: Translate complex algorithmic and AI-driven outputs into clear, actionable financial and operational recommendations directly for the VP.
Data Integrity & Architecture: Audit massive datasets to ensure extreme precision, as your model outputs will directly commit millions of dollars in spend.
Continuous Optimization: Constantly stress-test, refine, and modernize legacy planning sheets into automated, high-performance computing scripts.
Partnership: Partner closely with supply chain, procurement, finance, and engineering teams to evaluate scenario-based planning and long-range sourcing strategies.
What we need to see:
Bachelor’s degree within Operations Research, Industrial Engineering, Computer Science, Data Science, Mathematics, Statistics, Physics, or an equivalent quantitative engineering discipline (or equivalent experience).
Expert Modeling & Coding: Proficiency in Python (specifically libraries like Pandas, NumPy, SciPy) and MATLAB is non-negotiable.
15+ Years of Quantitative Experience: with Proven track record of personally building, running, and deploying complex mathematical, simulation, or financial models.
Hands-on AI/ML Experience: 2+ years of practical, hands-on experience designing, training, and deploying AI or algorithms based on learning from data (not just theoretical knowledge).
Ability to build complex, formula-dense spreadsheets, macro automation, and data models where rapid prototyping is required.
Extreme Detail Orientation: Demonstrated success in identifying edge-case anomalies in massive datasets that others miss.
Demonstrated experience presenting raw data and model architectures directly to VP-level leadership.
Ways to stand out from the crowd:
Advanced degree (Master’s or PhD) with a focus on optimization, simulation, or machine learning, or equivalent experience.
Prior background in the technology hardware, semiconductor, or electronics supply chain sectors.
Familiarity with capacity planning, inventory theory, or procurement logistics.
Experience deploying AI models into live cloud environments (e.g., AWS, Azure, GCP).
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.