(usa) Principal, Data Scientist

Walmart Walmart · Retail · Bentonville, AR

The Principal Data Scientist will architect and lead the design of Walmart's Retail Digital Twin ecosystem, focusing on integrating simulation engines, 3D frameworks, and agentic AI. This role involves building production-grade, scalable applications, defining the orchestration layer for agentic workflows, and driving software engineering excellence for a high-fidelity, scalable platform.

What you'd actually do

  1. Lead the design of a modular, distributed digital twin architecture that balances simulation fidelity with computational efficiency across diverse retail use cases.
  2. Evaluate and deploy the most effective simulation paradigms (Agent-Based, DES, Physics-based) and tools—whether leveraging AnyLogic, NVIDIA Omniverse, or building custom C++/Python-based engines from the ground up.
  3. Build production-grade, scalable applications that integrate real-time IoT data streams, ensuring the digital twin platform is performant and maintainable at an enterprise scale.
  4. Design the orchestration layer for agentic workflows, enabling intelligent entities within the twin to perform complex reasoning and autonomous optimization.
  5. Define the schema and governance for reusable process blocks (XML/JSON) and 3D CAD assets (USD/gLTF) to ensure interoperability across the organization’s pillars.

Skills

Required

  • C++
  • Java
  • Python
  • data structures
  • algorithms
  • distributed systems
  • Reinforcement Learning (RL)
  • Multi-Agent Systems (MAS)
  • Large Action Models (LAMs)

Nice to have

  • Machine Learning
  • optimization models
  • Python
  • Spark
  • Scala
  • R
  • scikit learn
  • tensorflow
  • torch

What the JD emphasized

  • architect of systems
  • agentic AI
  • Agentic AI Strategy
  • Agentic AI & Optimization
  • Reinforcement Learning (RL)
  • Multi-Agent Systems (MAS)
  • Large Action Models (LAMs)

Other signals

  • agentic AI strategy
  • orchestration layer for agentic workflows
  • complex reasoning and autonomous optimization
  • Reinforcement Learning (RL)
  • Multi-Agent Systems (MAS)
  • Large Action Models (LAMs)