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About This Role
Position Summary...
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What you'll do...
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Fraud Analytics \& Machine Learning \| Walmart Global Tech
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Help Shape the Future of Retail Intelligence
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Every day, millions of customers trust Walmart to deliver a seamless shopping experience. Behind that experience is one of the world's largest and most sophisticated commerce ecosystems—and protecting it from fraud requires exceptional data scientists.
As a Data Scientist III on the Inkiru Data Science team, you'll transform billions of data points into intelligent solutions that identify fraud, reduce risk, and protect both customers and the business. You'll partner with engineers, product managers, and business leaders to build analytics and machine learning solutions that directly influence decisions at global scale.
If you're energized by solving complex problems, uncovering meaningful insights, and seeing your work deployed into production, we'd love to meet you.
Why This Role Matters
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Fraud evolves every day—and so do we.
Our team combines advanced analytics, statistical modeling, machine learning, and AI to stay ahead of emerging threats while delivering a seamless experience for customers and associates.
Your work won't sit in a report.
It will power real products, influence strategic decisions, and help protect one of the largest retailers in the world.
What You'll Do
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You'll make an impact by:
- Detecting emerging fraud trends using large\-scale behavioral, transactional, and operational data.
- Building analytical models that improve fraud detection, risk management, and business decision\-making.
- Creating intuitive dashboards and visualizations that help leaders quickly understand key business metrics.
- Partnering closely with Product, Engineering, Data Science, and Business teams to bring analytical solutions into production.
- Exploring new analytical techniques—including machine learning and AI—to solve evolving business challenges.
- Improving data quality through validation, governance, and scalable analytical processes.
- Communicating complex findings through compelling storytelling that drives action.
What You'll Work With
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You'll leverage modern technologies including:
- Python
- SQL
- Spark
- Hive
- BigQuery
- Distributed computing platforms
- Tableau
- Power BI
- Machine Learning frameworks
- Cloud\-scale analytics platforms
- AI\-assisted analytics and modern data science workflows
Why You'll Love This Team
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You'll join a collaborative team that values curiosity, experimentation, and continuous learning.
Here you'll have opportunities to:
- Solve problems at one of the largest scales in retail.
- Work with experienced data scientists and machine learning engineers.
- Influence product strategy with data\-driven insights.
- Build solutions that move from concept into production.
- Learn new technologies while working on meaningful business challenges.
- Grow your career through mentorship, leadership opportunities, and cross\-functional collaboration.
What Success Looks Like
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Within your first year, you'll:
- Deliver analytical solutions that influence fraud strategy.
- Build trusted relationships across Product, Engineering, and Business teams.
- Create scalable dashboards and reporting used by leadership.
- Improve fraud detection through innovative analytical approaches.
- Contribute to production\-ready data science solutions that create measurable business value.
What You'll Bring
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We're looking for someone who combines technical excellence with curiosity and business acumen.
Preferred qualifications include:
- Strong analytical and statistical problem\-solving skills.
- Experience working with large, complex datasets.
- Proficiency in SQL and Python (R is also welcome).
- Experience with distributed computing technologies such as Spark, Hive, or BigQuery.
- Familiarity with data visualization tools including Tableau, Power BI, or Looker.
- Ability to communicate technical concepts clearly to both technical and non\-technical audiences.
- Passion for solving ambiguous problems using data.
- Bachelor's degree in a STEM field with 3\+ years of relevant experience, or a Master's degree with 1\+ years of experience.
Bonus Points If You Have
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- Experience in fraud analytics, financial risk, cybersecurity, or trust and safety.
- Experience building or deploying machine learning models.
- Knowledge of experimentation, causal inference, or advanced statistical techniques.
- Experience with Generative AI, LLMs, or AI\-assisted analytics.
- Experience working in cloud\-native data environments.
Why Walmart Global Tech
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Technology is reshaping how the world shops.
At Walmart Global Tech, you'll help solve problems that few companies can match in complexity or scale. Our teams build products and platforms that serve millions of customers, associates, and suppliers every day.
Here, your work has immediate impact.
You'll have access to world\-class data, modern technologies, and talented teammates—all while helping build the future of retail.
Whether your passion is analytics, machine learning, artificial intelligence, or data engineering, you'll find opportunities to learn, innovate, and grow your career.
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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: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years' experience in an analytics or related field. Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field. Option 3: 4 years' experience in an analytics or related field.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, Master’s degree 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...
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2101 Se Simple Savings Dr, Bentonville, AR 72712\-4304, 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.
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% 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 $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Sam's Club AI Hiring
Sam's Club has 2 open AI roles right now. They're hiring across Data Scientist. Based in Bentonville, AR, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
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