Financial Crime Analytics Officer

Requirements

• Experience with machine learning techniques applied to fraud detection.
• Familiarity with real-time transaction monitoring systems.
• Understanding of web traffic and RESTful APIs.
• Knowledge of AML/CTF and Fraud regulations.
• Experience with data visualization tools (e.g., Tableau, Power BI).
• Language Requirements
• Fluent English (mandatory)
• French and/or German (nice to have)
• SQ2

Additional Information

We are looking for a highly motivated Financial Crime Analytics Officer to join our Anti-Fraud & FinCrime Center team. In this role, you will be responsible for developing, enhancing, and monitoring data-driven solutions to detect and prevent fraudulent activities and financial crime. You will work closely with cross-functional teams including Compliance, Customer Care, and Software Engineering to design scalable models and actionable insights that protect our customers and the business. This role is part of the First Line of Defense, playing a critical role in proactively identifying, preventing, and mitigating fraud and financial crime risks. Develop, implement, and optimize data-driven models for fraud detection and prevention (e.g., ATO, APP fraud, money mule accounts, real time and near real time transaction monitoring). Analyze large datasets to identify suspicious patterns, anomalies, and emerging fraud typologies. Collaborate with stakeholders to translate business needs into analytical solutions. Build and maintain dashboards, reports, and KPIs to monitor fraud trends and model performance. Continuously improve detection strategies, balancing fraud risk and false positives. Support investigations by providing data insights and analytical support. Communicate findings and recommendations clearly to both technical and non-technical stakeholders. Document and maintain up-to-date documentation of processes, methodologies, and solutions, ensuring that projects and workflows are clearly understood by colleagues and meet internal and external audit requirements. Stay up to date with new fraud schemes, regulatory expectations, and industry best practices. Minimum 2 years of experience in fraud prevention and/or AML within a bank, fintech, or payment services company. Strong educational background, BS/MS in Computer Science, Mathematics, Statistics, Engineering, or a related field. Proficiency in SQL and at least one programming language such as Python and/or R. Experience working with large datasets and building analytical or predictive models. Knowledge of financial crime typologies (e.g., phishing, account takeover, money mule account). Excellent communication skills, with the ability to explain complex concepts to non-technical audiences. Team-oriented with strong collaboration skills. Strong problem-solving mindset with the ability to work independently and proactively.

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