Staff, Data Scientist (Pricing​/Reinforcement Learning

Position: Staff, Data Scientist (Pricing/Reinforcement Learning)
Position Summary...

What you'll do...

Immigration sponsorship is not available for this role. Applicants must be authorized to work for any employer in the United States without current or future visa sponsorship.

This is a full-time onsite role based in Bentonville, AR or Sunnyvale, CA; remote and hybrid options are not available.

About the Role:

The Walmart Global Tech Applied AI Data Science team is looking for a Staff Data Scientist to work alongside our team of data scientists to build complex, cutting edge, and scalable algorithms for our Pricing organization. We are shaping the future of retail and are ready to work closely with merchants to solve their problems.

About the Team:

The Applied AI team researches and develops advanced algorithms that power strategic decision-making across pricing, markdown optimization, price recommendations, price elasticity, assortment optimization, supply chain, replenishment, and store layout. You will work closely with data scientists, machine learning engineers, operations research scientists, and data analysts, while collaborating with business leaders, product managers, pricing strategists, software engineers, and data engineers to build AI-driven solutions at enterprise scale.

Our work combines techniques from forecasting, optimization, operations research, machine learning (classical ML, deep learning, reinforcement learning), causal inference, experimentation, and statistics to solve complex pricing and retail challenges. We develop innovative AI solutions that directly influence business strategy and customer experience, and our research is regularly published in top-tier conferences and journals.

What You'll Do:

* 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 Agent Ops:
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.

What You'll Bring:

* 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 Tensor Flow) and modern architectures for decision-focused AI.

* Practical experience with Explainable AI (XAI) and communicating complex model reasoning to non-technical business stakeholders.

* Excellent coding…