We are seeking a Staff Data Scientist to serve as a technical anchor for the Omni Price Recommendation Engine. You will lead the scientific design and end-to-end execution of high-frequency pricing systems that balance competitive positioning with long-term margin health. This is a high-visibility role requiring a deep mastery of Causal Inference, Reinforcement Learning, and Elasticity Modeling.

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.

Perform elasticity analysis across large data sets and category segments to empower data-driven pricing decisions.

Own the E2E Price Recommendation lifecycle, including scoping, feature engineering, causal modeling, experimentation (A/B testing), and ongoing performance optimization.

Develop advanced pricing and optimization solutions using:Causal Inference & Elasticity: Identification of treatment effects beyond simple log-log approaches (Double ML, Instrumental Variables, Uplift modeling).

Optimization & Reinforcement Learning: Multi-armed bandits, Deep RL (PPO, DQN) for sequential decision-making, and constrained optimization.

Deep Learning: Modern architectures for demand sensing and price-response curves.

Uncertainty Quantification: Bayesian approaches and conformal prediction to manage the risk of price changes.

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).

Apply graph-based modeling to capture cannibalization and halo effects across product hierarchies and spatial locations (GNNs, temporal graphs).

Establish strong evaluation and monitoring: Backtesting against historical price changes, drift detection, and calibration of price-response curves.

Drive best practices in AgentOps: Build Agentic workflows to enable chat-based price explainability and "what-if" scenario planning for Merchants.

Collaborate and Mentor: Partner with Product, Business, and Engineering to set technical direction and mentor the next generation of MLEs.