Analyse large datasets to identify fraud patterns, anomalies, and emerging internal fraud risks, and translate insights into actionable detection rules and controls
Monitor fraud alerts, trends and key metrics (e.g. detection effectiveness, case outcomes) through dashboards and reporting
Support investigations through data analysis, evidence gathering, and hypothesis testing
Conduct root cause analysis by linking data signals with underlying business processes and identifying control gaps
Partner with cross-functional teams (e.g. operations, product, business intelligence) to enhance detection capabilities and close control gaps
Recommend and support scalable improvements to fraud detection and monitoring, balancing effectiveness and operational efficiency
Identify opportunities to enhance detection logic, workflows and automation
Travel up to ~30% travel to support investigations, stakeholder engagement, process understanding and on ground reviews where required
Other ad-hoc duties as assigned
Requirements
Bachelor’s degree in Business Administration, Business Analytics, Economics, Finance, Accounting, Statistics, Mathematics, Information Systems, Computer Science, or any other relevant quantitative or business-related discipline
At least 1 year of experience in fraud detection, risk analytics, operations risk or similar roles preferred
Proficiency in SQL / Python, familiarity with generative AI tool is a plus
Strong analytical skills with experience working with large datasets to identify trends, anomalies and insights
Ability to bridge data insights with operational or business processes
Self starter with strong ownership, ability to proactively manage project progress to achieve business goals
Ability to thrive in a dynamic and collaborative environment
Experience in tech, e-commerce or platform businesses is a plus
