(usa) Staff, Data Scientist

Walmart · Retail · Crossman Respect Building CA SUNNYVALE, Bentonville, AR

Staff Data Scientist to lead the scientific design and end-to-end execution of high-frequency pricing systems using Causal Inference, Reinforcement Learning, and Elasticity Modeling. The role involves designing and deploying prescriptive ML models, performing elasticity analysis, owning the E2E Price Recommendation lifecycle, developing advanced pricing and optimization solutions, building explainable pricing systems, applying graph-based modeling, establishing evaluation and monitoring, and driving best practices in AgentOps.

What you'd actually do

  1. Design and deploy prescriptive ML models to address high-impact pricing and markdown needs, ensuring alignment with Walmart’s Global Tech strategy and EDLP integrity.
  2. Perform elasticity analysis across large data sets and category segments to empower data-driven pricing decisions.
  3. Own the E2E Price Recommendation lifecycle, including scoping, feature engineering, causal modeling, experimentation (A/B testing), and ongoing performance optimization.
  4. Develop advanced pricing and optimization solutions using: Causal Inference & Elasticity, Optimization & Reinforcement Learning, Deep Learning, Uncertainty Quantification.
  5. Build explainable pricing systems: Provide model interpretability and stakeholder-facing narratives on "why" a price recommendation was made (e.g., competitor move vs. inventory health).

Skills

Required

  • Causal Inference
  • Reinforcement Learning
  • Elasticity Modeling
  • ML model design and deployment
  • Feature engineering
  • Experimentation (A/B testing)
  • Optimization
  • Deep Learning (PyTorch or TensorFlow)
  • Uncertainty Quantification
  • Explainable AI (XAI)
  • Python
  • Software engineering fundamentals (testing, CI/CD, MLOps)

Nice to have

  • Graph-based modeling (GNNs, temporal graphs)
  • Spark
  • Feature Stores
  • Distributed training
  • Cloud environment (GCP/Azure)
  • Human-Centered AI (Dashboards)
  • Agentic Frameworks
  • LLM-based agents

What the JD emphasized

  • 8+ years in Data Science / Applied ML (or PhD + 5 years), with deep hands-on exposure to pricing, elasticity, or causal modeling.
  • Demonstrated experience delivering production-grade optimization models with measurable financial outcomes (e.g., Margin lift, Inventory turnover).
  • Strong knowledge of pricing dynamics: Seasonality, competitor indexing, promotional impact, regime changes, and price-point psychology.
  • Hands-on experience with deep learning frameworks (PyTorch or TensorFlow) and modern architectures for decision-focused AI.
  • Practical experience with Explainable AI (XAI) and communicating complex model reasoning to non-technical business stakeholders.
  • Agentic Frameworks: Experience deploying LLM-based agents to act as intermediaries between complex models and business users.

Other signals

  • end-to-end execution of high-frequency pricing systems
  • Causal Inference, Reinforcement Learning, and Elasticity Modeling
  • prescriptive ML models
  • optimization and Reinforcement Learning
  • AgentOps