Job Summary

The Fraud Data Analyst / Fraud Analytics Consultant will be a hands-on fraud data professional responsible for partnering with business and technology stakeholders to analyze fraud-related data, interpret requirements, and deliver accurate insights with a quick turnaround. The role combines practical fraud-domain knowledge, banking or financial services experience, and strong data analysis capabilities using Python, SQL, and Microsoft Excel. The consultant will analyze fraud patterns and risk indicators, validate data and analytical results, and communicate actionable findings to technical and non-technical stakeholders.

Key Responsibilities
• Partner directly with business and technology stakeholders to understand fraud-related business needs, priorities, and expected outcomes.
• Analyze structured data to identify fraud patterns, anomalies, trends, risk indicators, and actionable business insights.
• Use SQL to extract, join, validate, and reconcile data across relevant data sources.
• Use Python for data preparation, exploratory analysis, automation, and repeatable analytical workflows.
• Build clear analyses, summaries, trackers, and management-ready outputs using Microsoft Excel.
• Translate business questions into practical analytical approaches and provide rapid turnaround on time-sensitive deliverables.
• Validate data quality and analytical results, document assumptions, and communicate limitations or dependencies.
• Prepare concise presentations and communicate findings to both technical and non-technical stakeholders.
• Collaborate effectively with distributed teams while maintaining dependable coverage during U.S. business hours.
• Provide regular status updates and contribute to weekly reporting on progress, risks, issues, and deliverables.
• Apply practical knowledge of fraud concepts, patterns, risks, and analytical use cases to support business objectives.
• Develop actionable insights and stakeholder-ready deliverables rather than only producing raw data or generic reports.

Required Qualifications
• Hands-on professional experience in fraud analytics, fraud strategy, fraud operations analytics, fraud risk, or a closely related fraud domain.
• Strong data analysis experience, including data extraction, cleansing, validation, reconciliation, and interpretation.
• Prior experience in banking, consumer lending, financial services, or another regulated financial environment.
• Proficiency in Python for analytical use cases, including common data-manipulation techniques.
• Advanced SQL skills, including complex joins, aggregations, subqueries, and data-quality checks.
• Strong Microsoft Excel skills, including pivot tables, lookups, formulas, data validation, and analytical reporting.
• Ability to understand business requirements, ask effective clarifying questions, and convert business needs into deliverables.
• Strong written and verbal communication skills, including the ability to present analytical findings.
• Ability to work independently, manage priorities, and deliver high-quality work within short timelines.
• Practical understanding of fraud concepts, patterns, risks, and analytical use cases.
• Ability to provide reliable collaboration and coverage during agreed U.S. business hours.

Preferred Qualifications
• Experience supporting consumer finance, personal lending, credit, payments, account servicing, or collections fraud use cases.
• Familiarity with common fraud typologies, controls, loss drivers, alert or case data, and fraud performance metrics.
• Experience creating executive-ready dashboards, presentations, or recurring analytical reports.
• Experience working in a consulting, contract, staff augmentation, or distributed delivery model.
• Exposure to Agile delivery practices and cross-functional collaboration with product, operations, risk, compliance, and technology teams.