Data Analyst II - LMD Fraud Prevention

Walmart • Bentonville, AR

1d • Onsite

About The Position

The Senior Analyst, Fraud Detection Operations is responsible for identifying, analyzing, and mitigating fraudulent activity across the Spark Driver platform. This role combines advanced analytics, an investigative mindset, and real-world context to detect emerging threats and protect the integrity of the system. The ideal candidate thrives in ambiguous problem spaces, thinks like an adversary, and can translate complex behavioral patterns into actionable insights and defensible decisions. This role acts as a critical bridge between operational investigations, product/tooling enhancements, and broader fraud-prevention strategy.

Requirements

• Bachelor’s degree in Criminal Justice, Data Analytics, Business, Information Systems, or related field and 3+ years’ experience in fraud detection, investigations, risk analysis, or compliance—OR 5+ years’ equivalent experience without a degree.
• Proficiency in SQL and advanced Excel for large-scale data analysis, anomaly detection, and pattern identification.
• Experience with structured investigation processes, evidence documentation, and audit-ready case file management.
• Strong analytical and critical thinking skills with the ability to interpret complex, mixed-signal data and draw defensible conclusions.
• Effective written and verbal communication skills; able to distill complex findings into clear reports for cross-functional audiences.
• Bachelor's degree in Business, Engineering, Statistics, Economics, Analytics, Mathematics, Arts, Finance or related field
• 2 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.
• 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

• Analyze user behavior and platform interaction patterns to identify unusual activity, suspicious accounts, and emerging fraud vectors at scale.
• Apply core analytics fundamentals—segmentation, trend analysis, anomaly detection, correlation testing—to uncover fraud patterns across large datasets.
• Interpret mixed signals by combining data points such as GPS movement, device details, metadata, and behavioral indicators to assess risk.
• Apply real-world context and consider cultural, social, and environmental factors that automated systems might miss.
• Build and refine fraud indicators—measurable behaviors, thresholds, and composite signals that can be monitored through dashboards and automated models.
• Identify motivations and opportunities for fraud; translate hypotheses into testable data signals and validation frameworks.
• Stress-test queries by enumerating legitimate scenarios, then refine logic, thresholds, and exclusion rules to minimize false positives.
• Provide structured feedback and business requirements that support new fraud-signal development or detection-tool enhancements.
• Follow a structured investigation process, documenting each step clearly from initial detection through final decision with audit-ready case files.
• Use open-source research (OSINT) to look beyond internal data, confirm identities, and uncover external fraud clues or coordinated networks.
• Prepare clear, defensible reports and documentation that would stand up in audits or legal settings.
• Stay ahead of threats by thinking like a bad actor to anticipate how people might try to exploit the system next.
• Present operational insights to Product, Legal, Compliance, Engineering, and Care Operations to inform tooling improvements and policy updates.
• Provide operational context that supports broader fraud-prevention strategy and product design decisions.
• Produce clear, structured summaries of operational findings, QA trends, and SOP gaps that inform program updates.
• Ensure all documentation supports audit readiness and compliance expectations.

Benefits

• Competitive pay
• Performance-based bonus awards
• Medical coverage
• Vision coverage
• Dental coverage
• 401(k)
• Stock purchase
• Company-paid life insurance
• PTO (including sick leave)
• Parental leave
• Family care leave
• Bereavement leave
• Jury duty leave
• Voting leave
• Short-term disability
• Long-term disability
• Company discounts
• Military Leave Pay
• Adoption expense reimbursement
• Surrogacy expense reimbursement
• PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes
• Live Better U (Walmart-paid education benefit program)

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 1-10 employees