The main purpose of the Fraud Data Scientist is to support the delivery of the Transactional Fraud Mitigation service by leveraging data to discover new insights and develop solutions that allow for improved decision making. The overall objective is to provide customer value in fraud detection and analytics while establishing the organisation as a strategic partner in national fraud prevention. The Fraud Data Scientist will be required to maximise the performance of systems, toolkits, processes and associated data to contribute directly to the proactive detection of fraud and the reduction of fraud losses while minimising impact to genuine transactions. This involves the development, optimization, maintenance and evolution of fraud detection models at national level.

Key abilities include leveraging off of multiple skillsets, including data engineering, advanced computing, scientific methods, statistical computation, visualization, business communication, and domain expertise.

 Key stakeholders the Fraud Data Scientist will engage with are:

• Fraud Team (Business Owner, Analytics and Detection Team, Stakeholder Relationships)
• Internal departments (IT Ops:  Infrastructure, Networks, Applications, Database, Service Desk)
• Service providers, industry bodies & vendors

RESPONSBILITIES

FINANCIAL MANAGEMENT

• Demonstrate cost awareness and control and report on revenue, costs and volumes where applicable

STAKEHOLDER ENGAGEMENT   

• Build, influence, and manage key stakeholder relationships (internal and external) and expectations within all direct areas of responsibility.
• Deliver a high performing fraud detection model that meets detection matrix communicated to industry through:

1. Benchmark statistics of industry fraud levels
2. Measurement criteria adherence
3. Actionable insight to fraud mitigation
4. Continuous display of high value alerts

DELIVERY OF BUSINESS PROCESSES/PROJECTS

STRATEGIC ALIGNMENT

• Ensure individual deliverables are aligned to BU strategy, documented, clearly understood and regularly reviewed

STRATEGIC PAYMENTS SYSTEM ENHANCEMENTS

• Provide subject matter expertise for future data driven fraud/analytics related projects for TFM

DETECTION MODEL DEVELOPMENT AND MANAGEMENT

• Reviewing, analysing, and evaluating ideas, Develop and optimise core fraud detection models on our payment streams
• Manage client risk profiles by tuning detection models and thresholds to balance client risk requirements (hit rates, false positive rates, alert volumes etc)
• Ad hoc investigations of system output anomalies and general troubleshooting
• Monitor system performance according to multiple metrics at both an industry level and per client
• Manage data issues relating to detection and the incorporation of new data feeds when required/available

ACQUISTIION OF QUALITY DATA – TECHNICAL SUPPORT

• Data engineering support for the acquisition of quality fraud data
• Data wrangling evaluation to minimise validation errors
• Technical to business communication
• Ad hoc investigations of system output anomalies and general troubleshooting

THOUGH LEADERSHIP

Contribute to department publications on actionable insight in line with proven data science use cases.

COMPLIANCE AND RISK MANAGEMENT

• Policies and procedures: Develop, amend and implement relevant policies and procedures
• Compliance: Ensure compliance with relevant policies & procedures, regulation and legislation
• Risk: Ensure risks are reported on and mitigated

PEOPLE

Skills transfer and support         

• People Processes: Commitment to a fair and transparent performance management plan.
• People Development: Balanced participation in individual and team engagement sessions. Commitment and achievement in agreed career development plan