Principal, Data Scientist

Walmart · Retail · Bentonville, AR +2

This role focuses on building and deploying production ML systems and autonomous AI agents at enterprise scale within Walmart's Associate AI Experiences team. It involves architecting end-to-end ML systems, deploying agents with tool calling and multi-agent orchestration, designing evaluation frameworks, and working with NLP and computer vision pipelines. The role also requires mentoring and presenting insights to leadership.

What you'd actually do

  1. Build and deploy production ML systems end‑to‑end, including data pipelines, feature stores, model training, serving layers, monitoring, and feedback loops at scale
  2. Architect and deploy autonomous AI agents, including conversational assistants, predictive agents with tool calling, and multi‑agent orchestration systems using LangChain, Pydantic AI, and RAG patterns
  3. Design agentic evaluation frameworks to benchmark agent performance across task completion, code quality, and multi‑step reasoning accuracy
  4. Deploy NLP pipelines at enterprise scale, enabling semantic similarity across millions of records using BERT/SBERT embeddings and vector search (FAISS)
  5. Engineer computer vision systems for real‑time inference (YOLO, RT‑DETR, CLIP) with multi‑GPU training optimization

Skills

Required

  • ML algorithms (XGBoost, CatBoost, LightGBM, Random Forest, AutoML)
  • deep learning (PyTorch, transformer architectures)
  • NLP (BERT, SBERT, FAISS, RAG)
  • computer vision (YOLO, CLIP)
  • time-series forecasting (ARIMA, Prophet)
  • building and orchestrating multiple AI agents with LangChain, RAG, tool integration, and memory management
  • Multi-cloud ML platform proficiency across AWS SageMaker and GCP (Vertex AI, BigQuery, BigQuery ML)
  • Strong Python skills (Pandas, NumPy, scikit‑learn)
  • containerization expertise (Docker)
  • MLOps best practices
  • statistical analysis
  • experimental design
  • causal inference
  • translate complex analytical results into clear, actionable business recommendations

Nice to have

  • AI‑assisted development

What the JD emphasized

  • Architect end‑to‑end ML systems
  • Have built and shipped multiple autonomous AI agents
  • production ML systems
  • autonomous AI agents
  • multi-agent AI systems
  • enterprise scale

Other signals

  • build production ML systems
  • deploy autonomous AI agents
  • enterprise scale
  • multi-agent AI systems