Staff Data Scientist - Sam’s Fraud Prevention

Walmart; Bentonville (AR); On-site; USD 110,000 - 220,000; Full time

Job summary

Walmart is seeking a Staff Data Scientist for Sam's Fraud Prevention in Bentonville, AR. You will architect and productionize AI-driven fraud decisioning, linking cross-channel data to form a holistic fraud view, and deploy graph-based models to detect coordinated fraud rings.

You will own the ML lifecycle, build batch and real-time components, and collaborate with cross-functional teams to scale advanced fraud systems across Sam's Club operations.

Qualifications

• Master's degree or the equivalent in Analytics, Economics, Computer Science, or related field OR Bachelor's degree or the equivalent in Analytics, Economics, Computer Science, or related field plus 2 years of experience in analytics or a related field.

Responsibilities

• Architect and productionize AI-powered fraud decisioning system leveraging LLM orchestration and ML-based risk scoring.
• Build a unified fraud-aware customer view by linking multi-channel activity.
• Develop batch inference models to issue delayed risk recommendations for high-risk transactions.
• Engineer a large-scale distributed fraud graph platform with GraphDB/GraphFrames and GNNs.
• Lead development of an intelligent retail AI system to prevent in-club theft and shrink.
• Own full ML lifecycle from feature engineering to deployment and monitoring.
• Collaborate with Engineering, Fraud Operations, Policy, and Retail teams to scale fraud systems.

Skills

C++

Python

ML/DL models

Statistics & probability

Data visualization

AI ethics & fairness

Feature engineering

Graph analytics

Education

Master's degree in Analytics/CS/Economics or related field

Bachelor's degree + 2 years analytics experience

Tools

GraphDB

GraphFrames

Power BI

Tableau

Job description

What you'll do...

Position: Staff Data Scientist - Sam's Fraud Prevention

Job Location: 811 Excellence Dr, Bentonville, AR 72716

Duties: Architect and productionize Agentic AI-powered fraud decisioning system leveraging LLM orchestration, graph-derived embeddings, and ML-based risk scoring to aggregate multi-system signals, synthesize structured risk narratives, and generate automated accept/reject recommendations - materially reducing manual review dependency and decision latency. Design and deploy an ML-driven chargeback dispute automation agent that leverages structured feature extraction, document intelligence models, and evidence-ranking algorithms to auto-generate bank-ready dispute letters - improving recovery rates while reducing operational overhead. Build a unified cross-channel member profile by linking in-club, dotcom, returns, and transactional activity to create a holistic fraud-aware customer view, enabling more precise detection and reduced false positives. Develop and productionize an end-to-end batch inference model that complements the real-time ML pipeline by issuing delayed risk recommendations for high-risk transactions, strengthening overall fraud coverage. Engineer a large-scale distributed fraud graph platform leveraging GraphDB and GraphFrames to model multi-hop relationships across devices, payment instruments, addresses, and behavioral signals; implement Graph Neural Networks (GNNs) and unsupervised graph embeddings to detect coordinated fraud rings and synthetic identity clusters. Spearhead development of an intelligent retail AI system combining computer vision signals, behavioral ML, and anomaly detection algorithms to proactively prevent in-club theft - optimizing shrink reduction while preserving a frictionless customer experience. Establish feedback-driven fraud learning loops by integrating model outputs, manual review outcomes, and downstream signals into continuous system improvement. Own full ML lifecycle across multiple initiatives - from feature engineering and model design to deployment, monitoring, and business impact measurement. Partner cross-functional with Engineering, Fraud Operations, Policy, and Retail stakeholders to operationalize advanced fraud systems at scale.

Minimum education and experience required: Master's degree or the equivalent in Analytics, Economics, Computer Science, or related field OR Bachelor's degree or the equivalent in Analytics, Economics, Computer Science, or related field plus 2 years of experience in analytics or a related field.

Skills required: Must have experience with: coding in one of the following object-oriented programming languages: C++ or Python; Machine Learning and Deep Learning models, including CNN, Neural Networks and Bayesian Techniques; Statistics and Probability; performing data analysis and data collection using Python; identifying and applying metrics for measuring success and failure including F1, precision, recall and hypothesis testing; generating appropriate graphical representations of data and model outcomes; building scalable machine learning models for anomaly detection and Credit Risk; Data Visulaization in Python, Power BI or Tableau; AI Ethics, Model Fairness/Bias Monitoring; and Data and featuring engineering. Employer will accept any amount of graduate coursework, graduate research experience or experience with the required skills.

Rate of pay: $110,000.00 - 220,000.00/year

Wal-Mart is an Equal Opportunity Employer.

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.