Fraud Data Analyst

Key Responsibilities

• Data Instrumentation: Collaborate with Product and Engineering to define requirements for capturing fraud signals.
• Analysis & Detection: Identify anomalies, suspicious patterns, and credit fraud indicators like manipulated documents or synthetic identities.
• Investigation: Conduct deep dives into high-risk users and transactions to uncover new fraud typologies.
• Logic Implementation: Translate insights into fraud detection rules, monitoring systems, and risk models.

Requirements

• Years of Experience: Minimum of 5 years in fraud-related roles within Financial Services (Banking, Fintech, or Digital Lending).
• Education: Bachelor’s degree in Finance, Engineering, Data Science, Statistics, Computer Science, or a related field.
• Skills: Proficiency in identifying patterns and anomalies in behavioral and transactional data.
• Technical Knowledge: Understanding of fraud typologies (identity fraud, shell entities, document manipulation) and familiarity with querying/visualization tools.
• Collaboration: Ability to work across teams (Product, Risk, Engineering) and communicate recommendations clearly to stakeholders.

Bonus Point

• Experience in credit fraud or trust & safety analytics.
• Work history involving device data, behavioral signals, or real-time monitoring tools.
• Experience contributing to fraud models or scoring systems.