Financial Crime Analytics Officer

We are looking for a highly motivated Financial Crime Analytics Officer to join our Anti-Fraud & FinCrime Center 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.

Qualifications :

• 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.

Nice to Have

• 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)

Additional Information :

SQ2

Remote Work :

No

Employment Type :

Full-time