End-to-end ML model development for fraud detection, risk scoring, and customer segmentation — from feature engineering to deployment and monitoring.

• End-to-end ML model development for fraud detection, risk scoring, and customer segmentation — from feature engineering to deployment and monitoring.
• Deployed fraud ML models for SIM and service-only orders, combining behavioural and order features with threshold calibration in partnership with fraud operations.
• Contributed to a device payment scoring model that delivered $7.2M in FY25 benefit through improved risk decisioning and reduced fraud exposure.
• Built SMB customer segmentation using ML clustering, translating outputs into actionable personas that increased campaign engagement by 23%.
• Deep expertise in the full supervised learning lifecycle: feature engineering, model evaluation, interpretability, drift tracking, and governance.

Platform product ownership and architectural leadership for enterprise ML infrastructure — turning ML capability into scalable, governed, repeatable delivery.

• AI-Assisted Development & Agentic Tooling.
• Served as Technical Product Owner for Telstra’s Azure ML platform: defined roadmap, established reference architecture, and aligned stakeholders across business missions.
• Standardised MLOps practices across teams — reducing model delivery cycles and improving consistency from experimentation through to production.
• Established governance frameworks covering model lifecycle management, experiment tracking, and deployment standards on Azure ML.
• Bridged the gap between data science teams and engineering/platform stakeholders, translating technical requirements into delivery roadmaps.

Self-serve analytics, executive decision tools, and BI platforms that turn data into operational clarity — from KPI frameworks to addressable market tooling.

• Developed an addressable market and migration feasibility tool that reduced analysis cycle time from 1 month to 1 day, directly improving pipeline visibility.
• Built executive dashboards and operational decision tools across Telstra, Target Australia, CPA Australia, and NAB using Power BI and Tableau.
• Designed KPI frameworks and self-serve reporting layers that enabled non-technical stakeholders to access and act on data independently.
• Led analytics engineering across Hadoop/Hive environments, blending large-scale data with BI tooling for strategic business outcomes.

Robust data pipelines, warehouse architecture, and integration patterns across cloud and on-premise stacks — built for scale, quality, and analytical readiness.

• Delivered end-to-end ETL/ELT pipelines across Azure Data Factory, SSIS, Informatica, and Hadoop ecosystems (Hive, HDFS, Spark, Oozie).
• Worked across major cloud and warehouse platforms: Azure Synapse, AWS Redshift/Athena/S3, Oracle, SQL Server, Teradata, and PostgreSQL.
• Applied data quality practices including profiling, cleansing, reconciliation, and lineage support using Informatica BDQ and Alteryx.
• Designed and delivered data integration solutions for large enterprises including Telstra, NAB, and Target Australia over a decade of consulting.

Senior technical influence across cross-functional teams — product ownership, architecture decisions, stakeholder alignment, and enabling teams to deliver at scale.

• Led technical product ownership of an enterprise ML platform, setting strategic direction and translating vision into delivery across multiple missions.
• Managed stakeholder relationships at executive level, communicating complex technical trade-offs and roadmap decisions with clarity and impact.
• Drove Agile/Scrum delivery across long-running analytics and data engineering programmes in both consulting and in-house environments.
• Mentored data scientists and analysts, authored technical documentation and enablement materials, and established team standards for ML governance and delivery.