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About This Role
Position Summary...
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About Decision Sciences at Walmart:
Walmart moves with speed at scale. Doing that well requires more than good instincts or dashboards—it requires clear, defensible decisions embedded directly into how the business operates.
Decision Sciences is a newly formed organization within Walmart U.S. with a focused mandate: ensure decision makers know what is actually moving the business and why, and translate that insight into clear investment guidance that changes where Walmart deploys capital, engineering capacity, time, and organizational attention.
Decision Sciences partners directly with senior business leaders to move beyond descriptive analytics and into causal understanding, prioritized action, and faster execution, while maintaining or improving service levels across existing analytics and science teams.
The Group Director, Decision Sciences \- Merchandising is a senior leadership role responsible for standing up and leading the new Decision Sciences team, supporting the Merchandising organization.
This role sits at the intersection of business strategy, advanced analytics, experimentation, and talent leadership. The Group Director is accountable not just for analysis quality, but for whether the work changes decisions.
This is an enterprise shaping role, operating with VP level partners and leading teams that influence some of Walmart’s highest leverage decisions.What you'll do...
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Lead Decision Science at the Business Frontier
- Own Decision Sciences engagement for one or more major business domains (e.g., eCommerce, Marketplace, Marketing, Stores, Supply Chain).
- Partner directly with senior leaders to clarify outcomes, frame the right questions, and identify where science can most meaningfully direct investment.
- Ensure all work is explicitly tied to Customer Value Proposition priorities (Price, Assortment, Experience, Trust) and WM U.S. leadership AOP commitments.
Deliver Science That Changes Decisions
- Move teams beyond reporting and descriptive analytics into causal inference, experimentation, and decision guidance.
- Ensure outputs produce directional clarity—what to invest in more, what to deprioritize, and where intuition is wrong.
- Hold a high bar: work must change investment allocation, narrow focus, or contradict prevailing assumptions to be considered successful.
Own Experimentation Quality \& Rigor
- Ensure experiments and tests that reach senior leadership meet Decision Sciences standards for rigor, validity, and interpretability.
- Partner with shared experiment platforms and methodologies to maintain consistency and trust across the enterprise.
Build and Lead High‑Impact Teams
- Lead, coach, and grow senior data scientists, applied scientists, and analytics leaders.
- Build teams that are fluent in both business context and scientific method, capable of operating as trusted advisors rather than back‑office analysts.
- Set clear expectations around outcomes, prioritization, and time‑to‑value.
Operate Within a Pod‑and‑Platform Model
- Run a front‑office pod tightly aligned to business leaders, owning the relationship and outcomes.
- Leverage back‑office shared capabilities (foundational data, tooling, causal frameworks, experimentation standards) to reduce duplication and increase leverage.
- Contribute to raising the enterprise “floor” for decision quality through shared standards, training, and review.
Ruthlessly Prioritize for Impact
- Maintain a clear project hopper and capacity plan.
- Say “no” or “not yet” when work does not meet the Decision Sciences bar for leverage.
- Balance speed with rigor, and ambition with execution reality.
What Success Looks Like:
- Senior leaders can clearly point to moments where Decision Sciences changed what Walmart invested in or how it executed.
- Teams deliver fewer, higher‑impact analyses rather than broad, diluted coverage.
- Decision velocity improves without sacrificing quality.
- Trust in scientific outputs increases across leadership forums.
- The team is energized, focused, and operating with a shared identity and standards.
What You Bring: Experience \& Capability
- Deep experience leading data science, applied analytics, or economics teams in complex, scaled environments.
- Proven ability to translate advanced analysis into executive‑level decisions and action.
- Strong grounding in experimentation, causal inference, and decision science—not just metrics and dashboards.
- Experience partnering directly with senior business leaders on high‑stakes decisions.
Leadership Profile
- Operates comfortably at executive level with credibility and influence.
- Balances strategic thinking with operational execution.
- Willing to challenge intuition and surface uncomfortable truths with clarity and respect.
- Passionate about building teams and raising standards, not just delivering individual insights.
Mindset
- Obsessed with impact over activity.
- Comfortable narrowing focus rather than expanding scope.
- Energized by ambiguity and complex systems.
- Motivated by shaping how a Fortune‑scale company makes decisions.
Why This Role Matters The Group Director of Decision Science helps define how Walmart decides, not just what Walmart knows. If you are motivated by building something foundational, influencing decisions at massive scale, and pairing rigorous science with real‑world execution—this role offers a rare opportunity to do that work where it truly matters. At Walmart, we offer competitive pay as well as performance\-based bonus awards and other great benefits for a happier mind, body, and wallet. Health benefits include medical, vision and dental coverage. Financial benefits include 401(k), stock purchase and company\-paid life insurance. Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting. Other benefits include short\-term and long\-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more. You will also receive PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes. The amount you receive depends on your job classification and length of employment. It will meet or exceed the requirements of paid sick leave laws, where applicable. For information about PTO, see https://one.walmart.com/notices. Live Better U is a Walmart\-paid education benefit program for full\-time and part\-time associates in Walmart and Sam's Club facilities. Programs range from high school completion to bachelor's degrees, including English Language Learning and short\-form certificates. Tuition, books, and fees are completely paid for by Walmart.
Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to a specific plan or program terms.
For information about benefits and eligibility, see One.Walmart.
The annual salary range for this position is $195,000\.00 \- $370,000\.00 Additional compensation includes annual or quarterly performance bonuses. Additional compensation for certain positions may also include :
- Stock
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Minimum Qualifications...
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*Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.*
Option 1: Bachelor’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 8 years' experience in an analytics related field. Option 2: Master’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 6 years' experience in an analytics related field. Option 3: 10 years' experience in an analytics or related field.
4 years' supervisory experience.Preferred Qualifications...
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*Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.*
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, Supervisory, 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.Primary Location...
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601 Respect Dr, Bentonville, AR 72716, United States of America
Walmart and its subsidiaries are committed to maintaining a drug\-free workplace and has a no tolerance policy regarding the use of illegal drugs and alcohol on the job. This policy applies to all employees and aims to create a safe and productive work environment.
Salary Context
This $195K-$370K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Walmart, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($282K) sits 69% above the category median. Disclosed range: $195K to $370K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Walmart AI Hiring
Walmart has 36 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer. Positions span Bentonville, AR, US, Sunnyvale, CA, US, Elwood, IL, US. Compensation range: $79K - $370K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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