(USA) Staff, Data Scientist

Walmart•Bentonville, AR

2d•Onsite

About The Position

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.

Requirements

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.
Excellent coding skills in Python; strong grasp of software engineering fundamentals (testing, CI/CD, MLOps).

Nice To Haves

Experience with Causal Inference & Decision Science: Impact estimation, counterfactuals, and policy evaluation.
Advanced Graph Learning: Using GNNs to model cross-item elasticity and substitution patterns.
Large-scale Data/Compute: Experience with Spark, Feature Stores, and distributed training in a cloud environment (GCP/Azure).
Building Human-Centered AI: Dashboards for "driver decomposition" and "why the price changed" analysis.
Agentic Frameworks: Experience deploying LLM-based agents to act as intermediaries between complex models and business users.
Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 years' experience in an analytics related field.
Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years' experience in an analytics related field.
6 years' experience in an analytics or related field
Data science, machine learning, optimization models
PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics
Successful completion of one or more assessments in Python, Spark, Scala, or R
Using open source frameworks (for example, scikit learn, tensorflow, torch)
We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly.
The ideal candidate would have knowledge of accessibility best practices and join us as we continue to create accessible products and services following Walmart’s accessibility standards and guidelines for supporting an inclusive culture.

Responsibilities

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.

Benefits

incentive awards for your performance
401(k) match
stock purchase plan
paid maternity and parental leave
PTO
multiple health plans
competitive pay
performance-based bonus awards
medical, vision and dental coverage
401(k)
stock purchase
company-paid life insurance
PTO (including sick leave)
parental leave
family care leave
bereavement
jury duty
voting
short-term and long-term disability
company discounts
Military Leave Pay
adoption and surrogacy expense reimbursement
PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes.
Tuition, books, and fees are completely paid for by Walmart.