Hybrid Fraud Data Analyst

at Australian Payments Plus

• Job title: Fraud Data Analyst
• Job type: Contract
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Python, SQL
• Location requirements: Hybrid, Sydney, Australia

About the role

• Fraud Data Analyst supporting Fraud Operations to develop data analytics for fraud risk mitigation. Enabling data-driven decision making to strengthen payment security and operational effectiveness.

Responsibilities

• The purpose of the Fraud Data Analyst role is to support the Fraud Operations team by developing and operating measurement and data analytics capabilities to detect, assess, and mitigate fraud risks.
• This role enables data-driven decision making through the collection, analysis, and reporting of fraud-related data, contributing to risk management, quality assurance, and stakeholder engagement.
• Collect, maintain, and analyse measurement data to meet organisational information needs.
• Produce and distribute regular and ad hoc reports using analytical tools.
• Improve data collection processes and methods.
• Conduct risk management activities, including documenting risks, vulnerabilities, and mitigation actions.
• Support quality assurance through evidence collection, audits, and compliance reviews.
• Identify and address non-compliance or abnormal occurrences against organisational standards.
• Provide expert advice to inform planning and operational decisions.
• Gather and prepare multi-source data for analysis.
• Apply analytical and statistical techniques to identify trends, patterns, and insights.
• Manage stakeholder engagement, resolve issues, gather feedback, and strengthen relationships.

Requirements

• Bachelor’s degree in data Analytics, Statistics, Finance, or a related discipline.
• Relevant certifications in data analytics, fraud detection, or risk management (e.g. CFE, CAMS, or equivalent) are highly regarded.
• Proven experience in data analysis, ideally within financial services, payments, or fraud operations.
• Proficient in data analytics and statistical tools such as SQL, Python, R, Power BI, and Power Automate (or similar).
• Strong ability to interpret complex data sets and derive meaningful insights to support decision-making.
• Experience in risk management, quality assurance, or compliance processes is an advantage.
• Sound understanding of fraud detection methodologies, risk management principles, and relevant regulatory frameworks.
• Familiarity with data platforms, infrastructure, governance, and emerging technologies supporting analytics and fraud prevention.