PRINCIPAL, DATA SCIENTIST
Confirmed on the employer's own hiring board on Sep 28, 2026. First seen by Alion on Sep 25, 2026. Walmart scores A on the Alion truth index.
Position Summary...
Drives the execution of multiple business plans and projects by identifying customer and operational needs; developing and communicating business plans and priorities; removing barriers and obstacles that impact performance; providing resources; identifying performance standards; measuring progress and adjusting performance accordingly; developing contingency plans; and demonstrating adaptability and supporting continuous learning. Provides supervision and development opportunities for associates by selecting and training; mentoring; assigning duties; building a team-based work environment; establishing performance expectations and conducting regular performance evaluations; providing recognition and rewards; coaching for success and improvement; and promoting a belonging mindset in the workplace. Promotes and supports company policies, procedures, mission, values, and standards of ethics and integrity by training and providing direction to others in their use and application; ensuring compliance with them; and utilizing and supporting the Open Door Policy. Ensures business needs are being met by evaluating the ongoing effectiveness of current plans, programs, and initiatives; consulting with business partners, managers, co-workers, or other key stakeholders; soliciting, evaluating, and applying suggestions for improving efficiency and cost-effectiveness; and participating in and supporting community outreach events.What you'll do...
About the role
Merchandising Data Science builds the systems - both automated decision engines and decision support systems - that drive high-stakes merchandising decisions: how we select and curate items, manage space, plan and allocate inventory, flow inventory through the network, decide what and how much to buy, read the competitive landscape, and price. As a Staff Data Scientist, you own a single, hard problem end-to-end - not an abstract domain, but a specific question like "how do we estimate price elasticities better?", "how do we reduce forecast error for seasonal items a year out?", or "how do we incorporate all constraints at DC outbound so the planned flow can actually be executed, exceptions and all?" Depending on the problem, the answer is a system that decides automatically, or one that gives a merchant or planner the right recommendation, tradeoff, and evidence to decide well. You take the problem from a rough business question to a precise formulation to a production system, and you make sure it keeps getting better even on the days you're not in the room. You are an individual contributor with no direct reports - your leverage comes from raising the judgment and standards of the people working alongside you on that problem, not from personal output alone.
What you'll 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.
What you'll bring
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.
About Walmart Global Tech
Imagine working in an environment where one line of code can make life easier for hundreds of millions of people. That’s what we do at Walmart Global Tech. We’re a team of software engineers, data scientists, cybersecurity experts and service professionals within the world’s leading retailer who make an epic impact and are at the forefront of the next retail disruption. People are why we innovate, and people power our innovations. We are people-led and tech-empowered.
We train our team in the skillsets of the future and bring in experts like you to help us grow. We have roles for those chasing their first opportunity as well as those looking for the opportunity that will define their career. Here, you can kickstart a great career in tech, gain new skills and experience for virtually every industry, or leverage your expertise to innovate on a scale, impact millions and reimagine the future of retail.
We’re back at work
Walmart’s culture sets us apart, and we know being together helps us innovate, learn and grow great careers. This role is based in our Bangalore office for daily work, with the flexibility for associates to manage their personal lives.
Benefits
Beyond our great compensation package, you can receive incentive awards for your performance. Other great perks include a host of best-in-class benefits maternity and parental leave, PTO, health benefits, and much more.
Belonging
We aim to create a culture where every associate feels valued for who they are, rooted in respect for the individual. Our goal is to foster a sense of belonging, to create opportunities for all our associates, customers and suppliers, and to be a Walmart for everyone.
At Walmart, our vision is "everyone included." By fostering a workplace culture where everyone is-and feels-included, everyone wins. Our associates and customers reflect the makeup of all 19 countries where we operate. By making Walmart a welcoming place where all people feel like they belong, we’re able to engage associates, strengthen our business, improve our ability to serve customers, and support the communities where we operate.
Equal Opportunity Employer
Walmart, Inc., is an Equal Opportunities Employer - By Choice. We believe we are best equipped to help our associates, customers and the communities we serve live better when we really know them. That means understanding, respecting and valuing unique styles, experiences, identities, ideas and opinions - while being inclusive of all people.
Minimum Qualifications...
Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.
Minimum Qualifications:Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 5 years' experience in an analytics related field. Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 3 years' experience in an analytics related field. Option 3: 7 years' experience in an analytics or related field.Preferred Qualifications...
Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.