Responsibilities include, but not limited to:

As a Principal Data Scientist, you will spearhead the formation of a next‑generation fab digital twin team, driving innovation in an environment where NVIDIA Omniverse and OpenUSD‑based simulation technologies are entirely new to the organization. You will lead the exploration, definition, and delivery of cutting‑edge digital twin use cases, guiding cross‑functional partners through uncharted technical territories. This includes architecting scalable simulation pipelines, shaping data and model strategies, and establishing technical standards for real‑time, physics‑accurate virtual representations of advanced manufacturing operations. You will act as both a hands‑on technical leader and a strategic visionary-translating emerging technology capabilities into transformative business value while mentoring a growing team through rapid experimentation, ambiguity, and industry‑first innovation. You will work closely with data engineers, solution architect, external solution provider, and our stakeholders in construction, facility, planning teams in a collaborative manner to create highly accurate digital twins of our fabs and enable more advanced capabilities, e.g., Photorealistic and physics-based simulation, etc..

Digital Twin Development
Build and maintain data-driven models representing fab processes, equipment behavior, and subfab utilities.
Integrate IoT sensor data, MES (Manufacturing Execution System) data, and facility data into the digital twin platform.
Simulation & Optimization
Use simulation tools (e.g., NVIDIA Omniverse) to run what-if scenarios for layout changes, capacity planning, and process improvements.
Optimize fab workflows for cost, energy, and productivity.
Simulate continuous, time-aware construction -beyond traditional 3D BIM-with: Construction phasing & sequencing, Structural load simulation, Occupant movement and site logistics, etc.
OpenUSD‑based robotics training and simulation for better sim‑to‑real transfer
Predictive Analytics & Machine Learning
Develop algorithms for predictive maintenance, throughput optimization, and energy efficiency.
Apply statistical and ML techniques to forecast equipment failures, cycle time variations, and yield impacts.
Collaboration
Work closely with process engineers, facility planners, and IT teams to ensure seamless integration of digital twin solutions.
Support cross-functional projects involving AI, automation, and Industry 4.0 initiatives.

Required Skills

Master's/PhD degree in Operations Research, Computer Science, Data Science or other quantitative disciplines with strong emphasis on optimization, simulation, and algorithmic problem-solving.
12+ years of experience in 3D modeling and Simulation (experience in omniverse, or similar) .
Strong proficiency in Python, R, SQL, and data engineering.
Experience with machine learning frameworks (TensorFlow, PyTorch) and simulation tools.
Knowledge of semiconductor manufacturing processes and fab operations.
Familiarity with IoT data pipelines, MES systems, and cloud platforms (AWS, Azure)