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

Walmart Walmart · Retail · Bentonville, AR

Senior Data Analyst focused on building and optimizing fraud detection capabilities using SQL and Python pipelines. The role involves analyzing user behavior, developing risk-signal logic, experimenting with ML features and behavioral models, and making critical fraud deactivation decisions. It requires strong analytical skills, experience with structured investigation processes, and the ability to present findings to senior leadership.

What you'd actually do

  1. Analyzes user behavior and platform interaction patterns at scale to identify unusual activity, suspicious accounts, and emerging fraud vectors.
  2. Leverages fundamentals—including segmentation, trend analysis, anomaly detection, and correlation testing—to uncover sophisticated fraud patterns across massive, complex datasets.
  3. Builds and refines fraud indicators—including measurable behaviors, specific thresholds, and composite signals—for continuous monitoring through automated models and dashboards.
  4. Identifies fraud motivations and opportunities, translating theoretical hypotheses into testable data signals and rigorous validation frameworks.
  5. Presents high-level operational insights to Product, Legal, Compliance, Engineering, and Care Operations to directly inform technical tooling improvements and policy updates.

Skills

Required

  • SQL
  • Python
  • advanced Excel
  • large-scale data analysis
  • complex anomaly detection
  • advanced pattern identification
  • structured investigation processes
  • evidence documentation
  • audit-ready case file management
  • analytical and critical thinking skills
  • interpreting complex, mixed-signal data
  • drawing defensible, high-stakes conclusions
  • written and verbal communication skills
  • distilling highly complex technical findings into clear, actionable reports

Nice to have

  • gig economy, marketplace, or platform fraud-detection operations
  • OSINT techniques
  • device forensics
  • geolocation analysis
  • identity-verification processes
  • behavioral analytics
  • segmentation
  • statistical pattern recognition applied to fraud or risk scenarios
  • fraud-detection platforms
  • case management systems
  • rules-engine tools
  • architecting relational data structures and ontologies
  • presenting technical roadmaps to senior leadership

What the JD emphasized

  • fraud detection
  • ML features
  • behavioral models
  • audit-ready case file management
  • high-stakes fraud adjudication decisions

Other signals

  • fraud detection
  • ML features
  • behavioral models
  • SQL and Python pipelines