Principal, Data Scientist

Walmart Walmart · Retail · Bentonville, AR

This role focuses on building and deploying end-to-end production ML systems and autonomous AI agents, including conversational assistants and predictive agents with tool calling. It involves architecting multi-agent orchestration systems, deploying NLP and computer vision pipelines, and building time-series forecasting models at enterprise scale. The role requires expertise in various ML algorithms, deep learning, NLP, computer vision, time-series forecasting, multi-cloud ML platforms, and MLOps best practices.

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

  • Deep expertise in ML algorithms (XGBoost, CatBoost, LightGBM, Random Forest, AutoML)
  • Deep learning (PyTorch, transformer architectures)
  • Production experience with NLP (BERT, SBERT, FAISS, RAG)
  • Computer vision (YOLO, CLIP)
  • Time-series forecasting (ARIMA, Prophet)
  • Hands‑on experience 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
  • Advanced knowledge of statistical analysis, experimental design, and causal inference
  • Proven ability to translate complex analytical results into clear, actionable business recommendations for non‑technical stakeholders

Nice to have

  • AI‑assisted development

What the JD emphasized

  • built and shipped multiple autonomous AI agents
  • Level 5+ on Steve Yegge’s Vibe Coding scale and can ship production systems using AI‑assisted development
  • production ML systems
  • autonomous AI agents
  • multi-agent AI systems
  • enterprise scale
  • production experience
  • Hands‑on experience building and orchestrating multiple AI agents

Other signals

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