(USA) Senior, Data Analyst - Last Mile Delivery - Fraud Detection

Walmart•Bentonville, AR

1d•Onsite

About The Position

This role serves as the lead architect responsible for building, scaling, and optimizing fraud-detection capabilities across complex data ecosystems. It requires a blend of data analytics and behavioral science to architect relational data structures and ontologies while developing end-to-end SQL and Python pipelines (via fraud analytics tools) that power the detection engine. The core of the position involves developing risk-signal logic, building custom analytic applications, and experimenting with ML features and behavioral models to stay ahead of sophisticated threats. Beyond technical infrastructure, this role holds the autonomy to make critical fraud deactivation decisions and the executive presence to present technical strategies and findings to senior leadership, ensuring the fraud-detection roadmap aligns with global business goals.

Requirements

Holds a Bachelor’s degree in Criminal Justice, Data Analytics, Business, Information Systems, or a related technical field.
Brings 3+ years of specialized experience in fraud detection, investigations, risk analysis, or compliance—or 5+ years of equivalent high-level experience in lieu of a degree.
Demonstrates deep proficiency in SQL and advanced Excel for large-scale data analysis, complex anomaly detection, and advanced pattern identification.
Possesses proven experience with structured investigation processes, meticulous evidence documentation, and end-to-end audit-ready case file management.
Exhibits strong analytical and critical thinking skills, with a demonstrated ability to interpret complex, mixed-signal data and draw defensible, high-stakes conclusions.
Maintains effective written and verbal communication skills, capable of distilling highly complex technical findings into clear, actionable reports for diverse cross-functional audiences.
Option 1: Bachelor's degree in Business, Engineering, Statistics, Economics, Analytics, Mathematics, Arts, Finance or related field and 2 years' experience in data analysis, data science, statistics, or related field.
Option 2: Master's degree in Business, Engineering, Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field.
Option 3: 4 years' experience in data analysis, data science, statistics, or related field.

Nice To Haves

Experience in gig economy, marketplace, or platform fraud-detection operations.
Experience with OSINT techniques, device forensics, geolocation analysis, or identity-verification processes.
Background in behavioral analytics, segmentation, or statistical pattern recognition applied to fraud or risk scenarios.
Familiarity with fraud-detection platforms, case management systems, or rules-engine tools.
Proven expertise in architecting relational data structures and ontologies to support complex, high-scale data ecosystems.
Demonstrated ability to make high-stakes fraud adjudication decisions and present technical roadmaps to senior leadership.
Experience experimenting with machine learning/Artificial Intelligence features and sophisticated behavioral models to detect evolving fraud vectors.
Data science, data analysis, statistics, or related field.
Master’s degree in Business, Computer Science, Engineering, Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field.
Related industry experience (for example, retail, merchandising, healthcare, eCommerce).
Successful completion of assessments in data analysis and Business Intelligence tools and scripting languages (for example, SQL, Python, Spark, Scala, R, Power BI, or Tableau).

Responsibilities

Analyzes user behavior and platform interaction patterns at scale to identify unusual activity, suspicious accounts, and emerging fraud vectors.
Leverages fundamentals—including segmentation, trend analysis, anomaly detection, and correlation testing—to uncover sophisticated fraud patterns across massive, complex datasets.
Interprets mixed signals by synthesizing disparate data points such as GPS movement, device details, and behavioral metadata to build high-fidelity risk assessments.
Applies real-world context and considers cultural, social, and environmental factors to capture nuances that automated systems might overlook.
Builds and refines fraud indicators—including measurable behaviors, specific thresholds, and composite signals—for continuous monitoring through automated models and dashboards.
Identifies fraud motivations and opportunities, translating theoretical hypotheses into testable data signals and rigorous validation frameworks.
Stress-tests queries against legitimate user scenarios to refine logic, thresholds, and exclusion rules, effectively minimizing false positives.
Provides structured feedback and technical business requirements to drive the development of new fraud signals and the enhancement of detection tools.
Executes structured investigation processes, maintaining clear, audit-ready case files that document every step from initial detection to the final deactivation decision.
Leverages open-source research (OSINT) to look beyond internal data ecosystems, confirming identities and uncovering external fraud clues or coordinated criminal networks.
Prepares clear, defensible reports and technical documentation designed to stand up to rigorous internal audits or external legal settings.
Anticipates system exploits by thinking like a bad actor, proactively identifying vulnerabilities to stay ahead of evolving fraud tactics.
Presents high-level operational insights to Product, Legal, Compliance, Engineering, and Care Operations to directly inform technical tooling improvements and policy updates.
Provides deep operational context that guides broader fraud-prevention strategies and critical product design decisions.
Produces structured summaries of operational findings, QA trends, and SOP gaps to drive continuous program updates and process optimization.
Oversees all technical and operational documentation to ensure strict adherence to audit readiness and global compliance expectations.

Benefits

Competitive pay
Performance-based bonus awards
Health benefits (medical, vision and dental coverage)
401(k)
Stock purchase
Company-paid life insurance
Paid time off (PTO, including sick leave)
Parental leave
Family care leave
Bereavement
Jury duty
Voting leave
Short-term disability
Long-term disability
Company discounts
Military Leave Pay
Adoption and surrogacy expense reimbursement
Live Better U (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).