Financial Crime Data Science Analyst

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. Responsibilities The principal responsibilities of this role include, but are not limited to, the following: Financial Crime Analytics & Control Optimisation Perform detailed analysis of customer, transaction, screening, and financial crime data to identify trends, anomalies, emerging risks, and potential control gaps. Lead transaction monitoring scenario tuning and optimisation activities, including threshold calibration, effectiveness testing, sensitivity analysis, and refinement of detection logic. Conduct root cause analysis of financial crime control weaknesses, investigation outcomes, alert volumes, and operational performance indicators. Develop analytical insights that support Financial Crime Operations, Risk Management, and Compliance decision making. Monitor the effectiveness of financial crime controls and identify opportunities for enhancement through data-driven evidence. Support customer risk assessment, behavioural analysis, network analysis, segmentation, and suspicious activity detection initiatives. Assist in demonstrating end-to-end understanding and effectiveness of transaction monitoring controls during audits, reviews, and regulatory engagement activities. Data Science & Advanced Analytics Rigorously follows approved Data Science frameworks, executing, evidencing, and documenting all CRISP-DM lifecycle phases. Works in Agile/Kanban delivery environments, participating in stand-ups, reviews, retrospectives, and demonstrations of analytical outputs. Translates stakeholder needs into measurable analytical requirements, KPIs, success criteria, monitoring metrics, and decision logs. Uses Python, SQL and modern analytics tooling to develop maintainable, version-controlled pipelines and analytical solutions. Conducts statistical analysis, data mining and exploratory data analysis to generate descriptive, diagnostic, predictive and prescriptive insights. Designs, develops and validates analytical models including anomaly detection, classification, clustering, forecasting, behavioural analytics and risk scoring models. Evaluates and communicates model performance, limitations, confidence levels, assumptions, and business implications. Supports the responsible use of AI and machine learning solutions within the Financial Crime domain. Produces dashboards and reporting solutions that clearly communicate risk indicators, operational performance, control effectiveness and business outcomes. FCCM Platform & Data Management Act as a subject matter expert for Oracle FCCM and associated financial crime data assets. Support the ongoing administration, enhancement and optimisation of FCCM modules including Transaction Monitoring, KYC, Enterprise Case Management and Suspicious Activity Reporting processes. Support requirements gathering, testing, implementation and continuous improvement of Financial Crime systems and processes. Identify, track and support remediation of data quality issues across upstream and downstream systems. Develop automated controls, monitoring solutions, alerts and operational reporting capabilities. Support integration of Financial Crime data assets within the broader enterprise analytics landscape. Governance, Risk & Compliance Maintain comprehensive audit trails for analytics activities, model development, approvals, tuning decisions, deployments and handovers. Ensure compliance with GDPR, internal governance standards, financial crime policies, and data management requirements. Complete required pre-analysis risk, privacy, ethical and compliance assessments. Document methodologies, assumptions, validation outcomes and implementation decisions to support audit and regulatory review. Work collaboratively with First and Second Line stakeholders to ensure transparency of analytical approaches and control effectiveness. Support internal audits, compliance reviews and regulatory examinations by providing analytical evidence and documentation. Continuous Improvement & Innovation Identify opportunities to automate manual processes and improve operational effectiveness through analytics and technology. Research emerging financial crime analytics techniques, machine learning applications and industry best practices. Promote modern analytics capabilities across Financial Crime Operations. Contribute to the development and maturity of the organisation's Data Science and Financial Crime Analytics capabilities. Prioritise work that advances both financial crime control effectiveness and data science capability development. Demonstrate An Post Values and behaviours in your day-to-day work and address behaviour that supports/conflicts with them. Qualifications Honours degree in Data Science, Statistics, Mathematics, Computer Science, Finance, Risk Management, Economics, or a related discipline. Specialisation in Data Science, Artificial Intelligence, Machine Learning, Statistics, Financial Crime, or Risk Analytics is desirable. Professional certifications in AML, Financial Crime, Analytics, Data Science or Risk Management are advantageous. Experience 3–5 years' experience in Financial Crime Analytics, AML Operations, Data Analytics, Data Science, Risk Analytics, or a related field. Demonstrated experience working with transaction monitoring, customer screening, case management or financial crime systems. Experience managing projects through the full CRISP-DM/Agile lifecycle including governance and documentation requirements. Hands-on experience with Python and SQL for analytics, automation and data engineering. Experience working with Oracle FCCM/Compliance Studio or equivalent financial crime platforms. Experience performing scenario tuning, effectiveness assessment and optimisation of monitoring controls. Experience working within Agile/Kanban environments. Experience producing analytical reporting, dashboards and actionable business insights. Experience supporting audits, regulatory reviews and governance activities. Proven ability to communicate technical concepts and analytical findings to both technical and non-technical stakeholders. Critical Competencies (The following competencies are critical to the delivery of results and/or to superior performance in this role:) Problem Solving & Analysis Attention to Detail Technical & Professional Expertise Communication and Influencing Skills Teamwork Salary Salary range starting from €43,319 .Placement within the range reflects experience, skills, proficiency in the role, and internal alignment. Progression over time is linked to performance, development, and expansion in role scope. Benefits An Post Company Medical Scheme An Post Pension Scheme PRIP Bonu