Senior, Data Scientist

Walmart Walmart · Retail · Bentonville, AR

Senior Data Scientist at Walmart Global Tech focused on driving initiatives across forecasting, pricing, and space optimization. The role involves designing, developing, and deploying AI agents leveraging LLMs and advanced ML/DL models, implementing MLOps solutions, and building scalable, data-driven solutions for millions of customers. Requires expertise in Python, ML frameworks, MLOps, and cloud environments, with a focus on problem formulation, data preparation, advanced AI/ML development, model deployment, and research.

What you'd actually do

  1. Design and develop state-of-the-art algorithms, AI/ML models, and optimization techniques to generate actionable business insights.
  2. Design and develop state-of-the-art algorithms, AI/ML models, and optimization techniques to generate actionable business insights.
  3. Validate and monitor AI/ML models post-deployment, leveraging MLOps best practices such as CI/CD pipelines, performance tuning, and effective systems integration.
  4. Conduct cutting-edge research in machine learning, AI (including generative/agentic AI), optimization, and related areas.
  5. Deliver clear, impactful insights through compelling data visualizations, reports, and presentations.

Skills

Required

  • Python
  • Pandas/Polars
  • NumPy
  • PySpark
  • TensorFlow
  • PyTorch
  • GPU utilization
  • software development
  • mathematical/scientific computing
  • deploying large-scale AI/ML/DS solutions
  • parallelized cloud compute environments (GCP)
  • MLOps concepts
  • Google Vertex
  • MLFlow
  • ClearML
  • building and orchestrating ML pipelines
  • ETL/ELT data pipelines
  • data-driven applications at scale
  • transformers
  • embeddings
  • DevOps tools
  • Git/GitHub/GitHub Actions
  • infrastructure-as-code methodologies
  • Master’s degree in computer science, Machine Learning, Operations Research, Statistics, Optimization, Data Analytics, Mathematics, or a closely related quantitative field.
  • 3 years of professional experience in an ML or Optimization Model development-focused role.

Nice to have

  • agentic and multi-agent frameworks
  • tool-use
  • retrieval-augmented generation
  • policy learning
  • production integration
  • safety mechanisms
  • evaluation harnesses
  • experimentation platforms
  • sequential decisioning
  • A/B testing
  • contextual bandits
  • designing metrics for uplift measurement
  • serverless architecture
  • microservices implementation
  • API Gateway
  • FastAPI
  • VM-hosted solutions
  • logistics
  • supply chains
  • delivery operations

What the JD emphasized

  • deploying large-scale AI/ML/DS solutions
  • building and orchestrating ML pipelines
  • ETL/ELT data pipelines
  • data-driven applications at scale

Other signals

  • deploying AI agents leveraging LLMs
  • designing, developing, and deploying AI agents
  • advanced ML/DL models
  • MLOps solutions
  • large-scale AI/ML/DS solutions