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Developed a Python-based NLP (Natural Language Processing) model to match customer correspondence with customer records at a financial services client, using an ensemble of Levenshtein distance and TF-IDF (Term Frequency-Inverse Document Frequency) models. Improved name matching accuracy by 27%, reduced manual overhead by 70% and reduced average file processing time from 2 days to under 2 minutes.

Led a team of three consultants in designing, building and deploying a Python-based model to detect RIFA (Red Imported Fire Ant) nests within multispectral aerial imagery for the Queensland Government, to reduce the environmental impact of this invasive species. The model achieved 93% accuracy, 80% precision and 86% recall, with projected annual savings of $40m from reduced RIFA damage to agricultural crops.

As a Data Analyst, designed and automated Change Management reports for offshore Oil and Gas extraction projects, reducing report creation effort by 60%.

Designed and deployed 8 data models (using erwin and SQL) to support the introduction of Open Banking within an Australian financial services client.

Created a Python-based model to detect illegal sub-letting of social housing for a UK Government client, increasing fraud detection rates by 30%.

As a Data Analyst, analysed Smart meter job data (using SQL and Python) to identify common causes for failed jobs at a UK Energy industry client. Presented findings (using Tableau) and recommended remediations to client executives, resulting in a £100k reduction to additional job callout costs over a 6 month period.

Analysed application usage data to support a UK Water sector client's strategy to decommission unused applications. Identified 19 unused applications, resulting in a £1.3m reduction to annual licensing & support spend.

Managed multiple teams of SAP Functional and Technical consultants to implement enterprise transformation projects for utility industry clients using SAP ISU, CRM and Business Workflow.