(usa) Senior, Data Scientist

Walmart Walmart · Retail · Bentonville, AR +1

Senior Data Scientist role focused on building and deploying production ML systems and autonomous AI agents for enterprise-scale applications at Walmart. The role involves end-to-end system design, feature engineering, model training, serving, monitoring, and leading advanced development in agentic AI, reinforcement learning, and simulation. Key responsibilities include architecting large-scale AI systems, deploying autonomous agents with tool-calling and multi-agent orchestration, designing agentic evaluation frameworks, and deploying NLP pipelines. The role also emphasizes technical leadership, mentoring, and establishing best practices for AI system deployment.

What you'd actually do

  1. Build and deploy production ML systems end-to-end: data pipelines, feature stores, model training, serving layers, monitoring, and feedback loops at scale
  2. Provide technical visionand lead advanced development in agentic AI, reinforcement learning, and simulation.
  3. Architect and deploy large-scale AI systems and autonomous AI agents — conversational assistants, predictive agents with tool-calling, and multi-agent orchestration systems using LangChain, Pydantic AI, and RAG patterns
  4. Design agentic evaluation frameworks that benchmark agent performance across task completion, code quality, and multi-step reasoning accuracy
  5. Deploy NLP pipelines at enterprise scale — semantic similarity across millions of records using BERT/SBERT embeddings and vector search (FAISS)

Skills

Required

  • Python
  • scikit-learn
  • XGBoost/LightGBM
  • PyTorch/TensorFlow
  • Airflow
  • Dagster
  • Prefect
  • FastAPI
  • BentoML
  • TorchServe
  • Docker
  • Kubernetes
  • CI/CD
  • testing practices for AI/ML
  • monitoring
  • observability
  • statistics
  • experimental design
  • causal/measurement thinking
  • LangGraph/LangChain
  • Semantic Kernel
  • LlamaIndex
  • custom orchestrators
  • prompt strategies
  • structured outputs
  • reliability techniques
  • RAG design and optimization
  • LLM/agent evaluation methodologies
  • SQL
  • large-scale datasets

Nice to have

  • Pydantic AI
  • FAISS

What the JD emphasized

  • built and shipped multiple autonomous AI agents
  • architect end-to-end ML/AI systems
  • agentic AI
  • autonomous AI agents
  • agentic evaluation frameworks

Other signals

  • build and deploy production ML systems end-to-end
  • architect and deploy large-scale AI systems and autonomous AI agents
  • deploy NLP pipelines at enterprise scale
  • establish best practices for model validations, experimentation, and safe deployment of AI systems