Technical Financial Crime Manager

As we enter a phase of accelerated growth, we are seeking a Technical Financial Crime Manager to own, design, and scale our fraud and AML detection capabilities. This role sits at the intersection of data, engineering, and financial crime operations, with end-to-end accountability for ensuring our monitoring systems are technically robust, domain-accurate, and scalable across multiple markets.

• Define, build, test, and optimise fraud and AML detection rules, scenarios, thresholds, and models used in production systems.
• Translate complex datasets and domain insights into actionable detection logic embedded in monitoring and alerting platforms.
• Establish feedback loops between investigation outcomes and detection logic to continuously improve signal quality.
• Measure and manage detection performance using quantitative metrics (precision, recall, false positives, alert-to-case conversion, loss metrics).
• Own the end-to-end fraud and AML detection domain, ensuring alignment between prevention, detection, investigation, and remediation.
• Partner with Product and Engineering to embed detection logic into core systems and improve monitoring, alerting, and case management tooling.
• Identify gaps, risks, and inefficiencies, leading initiatives to strengthen controls and scale operations sustainably.

• 7+ years in financial crime roles in payments, fintech, banking, or financial services.
• Strong technical expertise in data analysis, including advanced SQL and experience working with large, complex datasets.
• Expert Python skills, including experience with libraries such as pandas, NumPy, scikit-learn, statsmodels, and/or model pipelines.
• Proven experience designing, building, and tuning risk detection systems (fraud, AML, or similar).
• Solid understanding of statistical modelling, machine learning, and/or time-series forecasting.
• Experience measuring and optimising detection performance using quantitative metrics.