Lead Data Scientist Financial Crime

• Sydney Melbourne Brisbane or Perth Location with Hybrid Working.
• Set the technical direction for fraud and financial-crime data science.
• Assure consequential decisions develop senior practitioners and turn portfolio investment into controlled outcomes.

Whats the role

As a Lead Data Scientist you will be the senior technical authority for fraud and financial-crime data science within Westpacs Enterprise Functions squad. You will set direction across a portfolio of detection monitoring and investigation capabilities ensuring outcomes are valid explainable operationally useful and well governed.

The portfolio spans transaction monitoring suspicious matter investigations scams anti-money laundering and counter-terrorism financing (AML/CTF) entity resolution typology and network analysis anomaly detection natural language processing (NLP) Generative AI neural networks and graph neural networks (GNNs). You will resolve the hardest methodological and architectural trade-offs and assure material work.

This is a lead individual contributor and craft-leadership role (AA Lead Specialist) not automatically a line-management position. You will operate through technical authority standards and influence delegate technical work and remain accountable for coherence and quality across initiatives. You will develop senior practitioners and represent the domain with senior business technology and risk stakeholders.

The impact you can make

• Shape a coherent portfolio across prevention detection prioritisation investigation and continuous control improvement to protect customers and the financial system.
• Direct investment towards measurable customer and control outcomes balancing investigative need data readiness feasibility regulatory risk and learning value.
• Improve portfolio quality through common standards independent challenge and comparable evaluation rather than isolated analytical experiments.
• Build enduring technical capability develop senior leaders and reduce dependence on individual knowledge holders.

What youll be doing

• Define and maintain the domain data science roadmap. Anticipate changes in criminal behavioural scams channels regulation data and technology and turn them into evidence-based priorities.
• Establish reference approaches combining rules typologies statistical detection classical machine learning graph analytics neural networks GNNs NLP and GenAI. Set principles for choosing simpler more explainable methods and common entity event graph feature label and decision-policy definitions.
• Resolve consequential choices involving anomaly detection weak or delayed labels entity resolution graph construction temporal validation thresholds causal claims and human decisions. Lead or sponsor dynamic graphs embeddings community and path analysis GNNs and hybrid rules-plus-ML systems.
• Set the direction for investigation summarisation information extraction narrative support typology discovery and investigator copilots. Define GenAI and agent evaluation for grounding factuality completeness consistency safety human review prompt injection and data-exfiltration risk.
• Connect model outputs to investigator queues escalation deferral feedback and approved human decision rights. Sponsor championchallenger approaches and independent challenge for material models typologies and GenAI applications.
• Review high-risk models graphs prompts agents pipelines and decision policies before material release or change. Set standards for data quality leakage prevention temporal validation calibration rare-event metrics fairness explainability reproducibility and documentation.
• Establish comparable evaluation linking technical performance to alert yield losses prevented investigation effort customer impact missed risk and control effectiveness. Ensure monitoring covers drift graph and entity changes typology decay fairness operational load and GenAI quality.
• Define triggers and governance for recalibration retraining prompt or typology changes rollback suspension and retirement. Lead the technical response to material model or data incidents and embed systemic lessons into standards and controls.
• Lead Responsible AI Model Risk privacy security AML/CTF records-management and audit assurance. Act as senior technical counterpart to legal compliance and second-line partners; provide credible governance regulatory and audit evidence within delegated authority.
• Lead difficult fairness proportionality vulnerable-customer and human-accountability decisions escalating beyond delegated authority. Preserve the distinction between indicators model inference and verified evidence; test sensitive-data use consequential failure and misuse under approved controls.
• Partner with financial-crime leadership investigators and product owners to align strategic requirements propose portfolio-level options and guide investment through delivery adoption and outcome realisation. Explain uncertainty scenarios benefits and control trade-offs to senior stakeholders.
• Resolve dependencies across data platforms case-management systems entity services cloud/AI platforms and control owners. Integrate solutions into sustainable operations and redirect or stop work where value is limited or risk unacceptable.
• Create reusable reference implementations feature and graph patterns typology components evaluation suites model cards and design guidance. Set review standards coach Senior Data Scientists towards Lead-level authority and build communities across science investigation engineering and assurance.
• Lead targeted horizon scanning and experimentation in adaptive anomaly detection multimodal and agentic AI temporal graphs and GNNs. Adopt new methods only with evidence of material advantage and represent the squad and craft in senior technical risk and professional forums.

What do I need

• Deep sustained expertise in advanced analytics statistics and machine learning with evidence of technical leadership across multiple consequential production use cases.
• Advanced-to-Mastery depth in at least one fraud or financial-crime analytical specialism with Advanced breadth across several areas: anomaly and behavioural detection; temporal network science graph embeddings and GNNs; entity resolution; NLP information retrieval and GenAI; neural networks and sequence modelling; or decision policy and human-in-the-loop design.
• Expert command of rare-event evaluation temporal validation uncertainty calibration model and graph explainability championchallenger testing and translating performance into decisions. Ability to assess interactions among data models rules prompts investigators capacity and controls.
• Strong working knowledge of production AI architecture MLOps/LLMOps monitoring observability controlled release reliability and lifecycle governance. A record of establishing standards assuring others work and resolving methodological disputes with evidence.
• Deep understanding of fraud and financial-crime detection and investigation including transaction monitoring scams AML/CTF suspicious matter investigations typologies red flags entity risk and network behavioural.
• Extensive experience leading fraud and financial-crime solutions or portfolios in a large complex organisation at a scale comparable to Westpac such as major financial services telecommunications or a similarly regulated enterprise.
• Strong understanding of criminal adaptation data and label limitations investigator workflows false-positive burden customer friction vulnerable-customer impacts and operational control design.
• Advanced understanding of Responsible AI Model Risk privacy AI security auditability AML/CTF and AUSTRAC expectations. Demonstrated capability to represent Westpac with external regulators within delegated authority explaining methods evidence limitations controls and remediation.
• Experience influencing senior business investigation technology and risk stakeholders on material technical and portfolio decisions. Ability to define a roadmap evaluate build/buy/partner options and prioritise competing investments against measurable outcomes.
• Enterprise-minded judgement technical courage and intellectual honesty. Make difficult calls expose uncertainty and dissent remain calm under consequence and balance innovation with proportionality customer rights and regulatory obligations.
• The ability to connect strategy to implementation detail influence without hierarchy and develop senior technical leaders. You need breadth to assure integrated solutions and recognised depth for consequential decisions not personal implementation of every specialist component.

What success looks like

• The portfolio delivers adopted measurable and sustainable detection monitoring and investigation capabilities with clear evidence of customer and control outcomes.
• Consequential analytical decisions withstand independent challenge and senior stakeholders understand the roadmap uncertainty trade-offs and risks.
• Quality improves across initiatives through common definitions reusable patterns disciplined review monitoring and timely intervention.
• Senior practitioners grow in technical authority knowledge is shared and the domain is less dependent on individual specialists.
• Decision rights remain clear: you own analytical direction method quality and model assurance not accountable business decisions authorised investigations compliance second-line risk or model-agnostic cloud security and platform infrastructure.

Ready to build what matters

Apply now and show us what you have built how you approached the problem and what changed because your solution made it into the hands of users!

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