Data Analyst - Risk & Fraud

Description Rakuten Viber is one of the most popular and downloaded apps in the world. Working with us means influencing hundreds of millions of users and shaping the journey toward becoming a true super-app. Our mission is to make people’s lives easier by enabling meaningful connections — from special moments with friends and family, to building businesses and pursuing passions. We’re looking for a Fraud Data Analyst who combines deep technical analytical skills with a strategic mindset and a curiosity for identifying fraud signals and driving trust in the fintech domain. You’ll partner closely with product, engineering, and support teams to turn complex data into automated detection models, scalable protocols, and measurable business protection. As a Fraud Data Analyst, you will be responsible for safeguarding the platform by identifying, investigating, and mitigating fraudulent activity. Your goal is to move beyond manual ticket handling by architecting scalable investigation protocols, automating detection logic, and providing strategic data insights that reduce the team's dependency on manual intervention. Responsibilities Lead complex transactional data into insights for suspicious patterns. Design and maintain Standard Operating Procedures (SOPs) for fraud investigations to ensure consistent, repeatable, and fast resolution times across the team. Build, automate, and maintain dashboards and reports to track platform health, user behavior, and key trends. Communicate findings clearly to stakeholders - Product, BE, etc. influencing both tactical and strategic decisions. Partner cross-functionally to translate data findings into actionable product and business decisions. Leverage AI and automation tools to accelerate analytical workflows and uncover deeper insights. Requirements 4+ years of experience in data analytics, with a relevant degree and a strong foundation in statistics. 2+ years in Risk Operations, or Trust & Safety (Fintech/Payments a plus), with a proven track record of preventing €1M+ in annual fraudulent losses. Proven ability to translate complex fraud patterns into high-performance heuristic rules and decision trees that function in real-time environments without degrading system latency. Strong command of SQL, Python, and data visualization tools (Tableau). Proven track record of turning manual processes into automated workflows. Proficient in using AI/LLM for advanced fraud detection, pattern recognition, accelerating analytical workflows. Experience working with a transaction volume of at least 10M/day at scale. Advantages Experience with European and International fintech regulations (e.g. GDPR) and their impact on fraud monitoring and user data privacy. Experience mentoring analysts or leading analytical projects. A deep interest in fraud, AI, or communication apps.