PRINCIPAL, DATA SCIENTIST
Walmart · Posted Oct 14
Retail, e-commerce, grocery, pharmacy, and membership warehouse services
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About the role
Merchandising Data Science at Walmart builds automated decision engines and decision-support systems for high-stakes merchandising decisions such as item selection and curation, space management, inventory planning, allocation, flow, buying, and pricing. The team applies optimization, simulation, forecasting, and applied ML/causal inference to solve problems like improving price elasticity estimation and reducing forecast error for seasonal items. This Principal Data Scientist role owns a single hard problem end-to-end, from business question to precise formulation to production system, and acts as an individual-contributor leader who raises the judgment and standards of the people working alongside them.
What you will do
- Own one hard, well-defined problem end-to-end — e.g., "how do we estimate price elasticities better?" or "how do we reduce forecast error for seasonal items a year out?" — from rough business question to precise formulation to production system.
- Define what a good answer looks like: decision variables or recommendation logic, objective function, constraints, fallback behavior, and measurable success criteria.
- Decide whether the problem calls for a fully automated decision engine or a decision support system that augments a human decision-maker, and design accordingly.
- Choose and implement the right solution approach — optimization, heuristics, simulation, forecasting, causal/statistical inference, or a hybrid — and defend the choice on evidence.
- Build the production system, not a prototype, integrating with the merchandising, planning, and platform systems it depends on.
- Establish patterns, methods, and quality standards on this problem that other people working on it (Senior DS, engineers) adopt and extend.
- Define the metrics and run the experiments that prove the answer is actually better — this may be estimation accuracy, forecast error, executability of a plan, margin, sell-through, or trust/adoption of a recommendation, depending on the problem.
- Create mechanisms — documentation, reusable components, decision frameworks — so continued progress on the problem does not depend on you personally.
- Proactively surface the next-most-important version of the problem before being asked (e.g., once elasticity estimation improves for core items, is the next gap seasonal or new items?).
- Use AI-accelerated development (copilots, agents, evaluators) to speed iteration while holding a high bar for correctness and maintainability.
- Communicate the problem, tradeoff, recommendation, and "so what" clearly to engineering leads, merchants, and business stakeholders.
Skills used in this role
What the employer is looking for
- A track record of taking a hard, specific business question — not a vague domain — and shipping a system, automated or decision-support, that measurably improved the answer.
- Solid depth in optimization/decision methods (mathematical programming, constraint programming, heuristics, or simulation) and applied ML/forecasting/causal inference where relevant.
- Experience with problems like: demand or elasticity estimation, long-horizon or seasonal forecasting, constrained planning/execution (e.g., DC outbound flow), assortment or allocation optimization, or competitive/price response modeling — in merchandising, retail, supply chain, or a comparable setting.
- Comfort building for two different kinds of "users" — a machine executing a decision automatically, and a human (merchant, planner, analyst) who needs the right recommendation and evidence to decide well.
- Demonstrated ability to make others working on the same problem better — through review, mentorship, or reusable standards — not just to produce more yourself.
- Sound judgment on when to pursue optimality vs. a robust heuristic, and when to automate a decision vs. support a human making it, with the ability to explain the tradeoff.
- A bias for iteration with accountability: you stay with a problem through adoption and validation, not just through ship.
- No people-management experience required — this is an individual-contributor leadership role built on influence, not authority.
- Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related f...
Benefits and support
- incentive awards for performance
- maternity and parental leave
- PTO
- health benefits
- flexibility for associates to manage their personal lives
About Walmart
Walmart Inc. is an American multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores. As one of the world's largest companies by revenue, it serves millions of customers daily through physical stores and robust e-commerce platforms. The company is driven by a commitment to everyday low prices and an expanding suite of digital and convenience-focused services.
- Industry
- Retail
- Company size
- 2000000+ employees
- Founded
- 1962
- Location
- Bentonville, Arkansas, USA
- Funding stage
- Public Company
Leadership
President and CEO
Executive Vice President and Chief Financial Officer
Executive Vice President, Global Chief Technology Officer and Chief Development Officer
Executive Vice President and Chief Growth Officer
President and CEO, Walmart U.S.
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