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inDrive is seeking a Senior Anti-Fraud Specialist to own detection and investigation of fraud across its ride-hailing, delivery, and city-to-city products. You will identify and mitigate sophisticated fraud schemes including ban evasion through multi-accounting, GPS spoofing, ride manipulation, promo and referral abuse, payment fraud, and driver–passenger collusion.
Key responsibilities include:
• Analyze transactional and behavioral data using SQL and Python to identify suspicious patterns and emerging fraud schemes
• Develop, tune, and improve fraud detection rules and anti-fraud dashboards
• Evaluate the quality and effectiveness of detection rules by tracking precision and other key performance metrics
• Manage fraud cases end-to-end, from identifying suspicious activity to investigation, documentation, and escalation of confirmed incidents
• Conduct risk assessments of new and existing product features in collaboration with Product teams
• Analyze and monitor fraud losses, identify key sources of losses, and develop plans to reduce them
• Propose and implement fraud risk mitigation measures together with Product and other cross-functional teams
This is an individual contributor role requiring strong analytical skills, independence in driving investigations, and the ability to work across ambiguous, complex fraud scenarios. You will partner with Product, Engineering, and other teams to build and refine the company's fraud defense posture.
REQUIREMENTS:
• 2+ years of experience in anti-fraud, risk analytics, or a related analytical field
• Strong proficiency in SQL and Python for analyzing large datasets
• Solid understanding of statistics and the ability to apply statistical methods to behavioral analysis and anomaly detection
• Experience working with data and the ability to independently identify suspicious patterns and formulate hypotheses
• Strong analytical thinking, attention to detail, and a structured approach to investigations
• Ability to work in ambiguous environments and independently drive investigations from initial detection to actionable conclusions and recommendations
• English — B2 or higher
