Financial Crime Data Science Analyst

An Post Fingal, Dublin, Ireland · €43K/yr

Utilities · 10,001+ employees

8 h ago

Mid (2-5 yrs) Full-time Ireland

About the role

The analyst will partner with cross-functional teams to deliver data-driven insights and automated solutions to strengthen financial crime prevention. They will manage analytics initiatives throughout the CRISP-DM lifecycle to improve control effectiveness and operational efficiency.

What they look for

Anti-money laundering Transaction monitoring Data science Python SQL Machine learning Statistical analysis Financial crime compliance Data engineering Scenario optimisation Predictive modelling Data visualisation Agile Risk analytics Regulatory compliance Case management

Requirements

Candidates must hold an honours degree in a relevant discipline and possess 3-5 years of experience in financial crime analytics or a related field. Proficiency in Python, SQL, and experience with financial crime platforms like Oracle FCCM are essential.

Benefits

Company medical scheme Pension scheme Bonus scheme Paid maternity leave Paid paternity leave Employee assistance programme Digital gym Cycle to work scheme Tax saver travel pass

Full description

Please note: This is an evolving and developing role and may change over time in line with business needs.

The Financial Crime Data Science Analyst partners with Financial Crime Operations, Compliance, Risk, Technology, and business stakeholders to deliver data-driven insights, automated solutions, and sophisticated analytical capabilities that strengthen the organisation's financial crime prevention and detection framework.

The role combines specialist expertise in Financial Crime Operations, Anti-Money Laundering (AML), transaction monitoring and Financial Crime Compliance Management (FCCM) systems with modern data science techniques. The analyst manages analytics initiatives throughout the full CRISP-DM lifecycle, applying statistical analysis, automation, machine learning, and data engineering practices to improve control effectiveness, optimise monitoring scenarios, identify emerging risks, and enhance operational efficiency.

A key aspect of the role is ensuring the ongoing effectiveness and continuous improvement of financial crime controls through data analysis, scenario optimisation, automation, and evidence-based decision making, while maintaining robust governance, auditability, and regulatory compliance.