Technical Fraud Analyst

Job description

Purpose of role

To strengthen Shop2Shop's fraud prevention capability by leveraging advanced analytics, machine learning and data science techniques to identify, predict and prevent fraudulent activity. The role transforms complex data into actionable insights that improve fraud detection systems and minimise financial risk.

Why you will thrive in this role

You are passionate about data, naturally analytical and enjoy solving complex problems. You thrive in a fast-paced environment where innovation, technology and continuous improvement drive better fraud prevention outcomes.

What you’ll do

• Analyse large datasets to identify fraud patterns and anomalies.
• Develop and refine predictive fraud detection models.
• Research and implement new fraud detection methodologies.
• Monitor and improve fraud detection system performance.
• Ensure data integrity and security.
• Deliver actionable insights and recommendations.
• Implement real-time fraud analytics.
• Collaborate with cross-functional teams.
• Develop systems to identify unusual transactions and behaviours.

 Core competencies

• Analytical thinking
• Critical thinking
• Attention to detail
• Problem solving
• Communication
• Adaptability
• Ethical integrity
• Innovation
• Time management
• Collaboration

Skills

• SQL, Python, R and Java
• Statistical analysis
• Machine learning and AI
• Data modelling
• Data visualisation
• Large dataset analysis
• Fraud detection techniques
• Microsoft Office

 

Qualifications and experience

• Bachelor's degree in data science, computer science, statistics, mathematics, engineering or related field.
• Minimum 2 years' experience within fraud or data analytics.
• Experience working with large datasets and databases.

Bottom of form

Other requirements

• Strong workplace setup with uninterrupted connectivity for remote collaboration
• Ability to manage highly confidential information
• Continuous learning mindset
• Ability to work under pressure and meet deadlines

 

Posted on 30 Jun 09:21, Closing date 29 Aug