(usa) Distinguished, Data Scientist

Walmart · Retail · Bentonville, AR +2

This role focuses on architecting and deploying large-scale AI systems and autonomous AI agents, including conversational assistants and predictive agents with tool-calling. It involves building end-to-end ML systems from data pipelines to serving layers and monitoring, with a strong emphasis on agentic AI, reinforcement learning, and simulation. The role also includes deploying NLP pipelines 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

  • ML, simulation, optimization, agentic AI
  • feature engineering
  • production deployment and monitoring
  • architect end-to-end ML/AI systems
  • autonomous AI agents
  • production systems
  • AI-assisted development
  • statistical rigor
  • practical business impact
  • agentic AI
  • reinforcement learning
  • simulation
  • conversational assistants
  • predictive agents with tool-calling
  • multi-agent orchestration systems
  • LangChain
  • Pydantic AI
  • RAG patterns
  • agentic evaluation frameworks
  • NLP pipelines
  • semantic similarity
  • BERT/SBERT embeddings
  • vector search (FAISS)
  • model validations
  • experimention
  • safe deployment of AI systems
  • 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)
  • tool integration
  • memory management
  • Multi-cloud ML platform proficiency — AWS SageMaker AND GCP (Vertex AI, BigQuery, BigQuery ML)
  • Python skills (Pandas, NumPy, Scikit-learn)
  • containerization (Docker)
  • MLOps practices
  • Advanced statistical analysis
  • experiment design
  • causal inference
  • Masters degree in Statistics, Economics, Mathematics, Computer Science, Information Technology, or related field AND 8+ years’ experience in data science or Machine Learning roles with demonstrated impact

Nice to have

  • PhD in AI, Machine Learning, Computer Science, Information Technology, Statistics, Applied Mathematics, Econometrics, or related field with relevant industry experience

What the JD emphasized

  • productionized solutions
  • built and shipped multiple autonomous AI agents
  • ship production systems
  • production Agentic systems
  • deploy autonomous AI agents
  • build intelligent platforms, agents and ML-powered solutions
  • Deploy NLP pipelines at enterprise scale
  • Deploy production-grade AI systems at scale
  • production experience with NLP
  • Hands-on experience building and orchestrating multiple AI agents

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
  • Experience deploying production-grade AI systems at scale