Interested in this Data Scientist role at Sam's Club?
Apply Now →Skills & Technologies
About This Role
Position Summary...
-----------------------
At Sam’s Club, we’re member-obsessed — and data is at the heart of how we deliver value to both our members and our supplier partners. The Measurement, Insights and Data Strategy (MINDS) team powers the Sam’s Club Member Access Platform (MAP) by turning 100% traceable member journey data into actionable insights, scalable media solutions, and measurable business impact.
As a Staff Data Scientist on the Media Science & Experimentation team, you will lead the development of ML-driven audience targeting solutions and help define how experimentation drives onsite media performance. Your work will directly influence how top-tier advertisers activate against Sam’s Club members, how campaigns are optimized in-market, and how value is communicated to executive stakeholders. This is a highly visible role combining advanced machine learning, experimentation rigor, and executive storytelling at scale.
What you'll do...
---------------------
What you’ll do
- Deliver ML-powered audience solutions that drive advertiser outcomes
Design, build, and deploy high-impact propensity and lifetime value (LTV) models that meaningfully improve targeting effectiveness, ROAS, and campaign performance for top advertisers (e.g., P&G, PepsiCo).
- Own technical partnerships with strategic advertisers
Act as the primary data science partner for large advertisers and measurement partners — shaping campaign objectives, defining success metrics, and translating complex modeling approaches into clear, business-relevant outcomes.
- Scale models from concept to production
Lead the end-to-end lifecycle of ML solutions — moving from POC to production by building parameterized, automated, and scalable pipelines in close collaboration with Engineering and Product.
- Advance experimentation for onsite display media
Design and analyze rigorous A/B tests to evaluate onsite display ad strategies, validate hypotheses, and inform product and media decisions that improve member experience and monetization.
- Automate insights through GenAI-powered storytelling
Architect and deliver AI-driven solutions that automatically generate executive-ready narratives and presentation materials, dramatically reducing manual reporting while elevating insight quality.
- Influence executive decision-making through storytelling
Translate complex analytical findings into compelling narratives for VP-level and above stakeholders — clearly articulating ROI, business impact, and strategic implications.
What you’ll bring
- Proven experience building ML models that drive real business impact
3+ years of hands-on, non-academic experience developing and deploying ML models end-to-end — particularly propensity and/or LTV models using tree-based methods (XGBoost, LightGBM).
- Strong experimentation and statistical judgment
Deep expertise in A/B testing design and analysis, including power analysis, hypothesis testing, and advanced methods (bootstrapping or Bayesian approaches are a plus).
- Experience operationalizing analytics at scale
Comfort turning analytical ideas into production-ready solutions using Python and SQL, with an emphasis on automation, parameterization, and repeatability.
- GenAI and automation mindset
Demonstrated ability to leverage LLMs or GenAI tools to automate narrative generation, reporting, or presentation creation from data.
- Ability to bridge data science and executive storytelling
A track record of communicating complex analytical results to senior business stakeholders in a clear, persuasive, and strategic way.
- Retail media or AdTech exposure (strong plus)
Familiarity with Retail Media Networks, AdTech platforms, or closed-loop measurement environments is highly valued.
- Modern data & ML tooling experience
Experience with GCP (BigQuery, Dataproc, Vertex AI preferred), and familiarity with engineering best practices such as CI/CD, Docker, PySpark, or scalable data pipelines.
- Strong quantitative foundation
A Master’s degree or PhD in Statistics, Economics, Computer Science, Mathematics, or a related field is preferred.
At Sam's Club, 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, parental leave, family care leave, bereavement, jury duty, and voting. 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.
- Other benefits include short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more.
Live Better U is a company 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.
Bentonville, Arkansas US-09930: The annual salary range for this position is $110,000.00 - $220,000.00
San Bruno, California US-08848: The annual salary range for this position is $143,000.00 - $286,000.00 Additional compensation includes annual or quarterly performance bonuses. Additional compensation for certain positions may also include Regional Pay Zone (RPZ) (based on location).
ㅤ
ㅤ
ㅤ
ㅤ
Minimum Qualifications...
-----------------------------
*Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.*
Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 years' experience in an analytics related field. Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years' experience in an analytics related field. Option 3: 6 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.*
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.
Primary Location...
-----------------------
2101 Se Simple Savings Dr, Bentonville, AR 72712-4304, United States of AmericaWalmart 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.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 33,423 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Sam's Club, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,400 based on 341 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $225,000. Disclosed range: $110K to $286K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Sam's Club AI Hiring
Sam's Club has 1 open AI role right now. They're hiring across Data Scientist. Based in Bentonville, AR, US. Compensation range: $286K - $286K.
Location Context
Across all AI roles, 7% (2,320 positions) offer remote work, while 30,984 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 33,423 open positions tracked in our dataset. By seniority: 3,283 entry-level, 20,769 mid-level, 6,381 senior, and 2,990 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,320 positions). The remaining 30,984 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 33,423 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (30,275), AI Software Engineer (749), AI Product Manager (741). 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 (3,283) are outnumbered by mid-level (20,769) and senior (6,381) 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 2,990 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,320 positions), with 30,984 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (21,235 postings), Aws (11,126 postings), Rust (9,803 postings), Python (4,999 postings), Azure (3,220 postings), Gcp (2,707 postings), Prompt Engineering (1,817 postings), Openai (1,487 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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.