Client-facing consulting role providing data analytics solutions to wider forensics, financial crime and insolvency teams. Managed end-to-end data analysis pipelines in several high-profile cases, liaised with clients, and authored reports under tight deadlines.

Responsible for all analytics and reporting for ASB’s credit card fraud prevention rulesets. Regularly work with large, near-real-time datasets responding to mission critical fraud events affecting our customers. My role also requires me to coach/mentor my fellow team members and work on improving the team with self-driven future-focussed projects.

Started as a graduate in IT advisory before moving to the newly formed KPMG Lighthouse data analytics division in January 2019. Worked across a range of projects, with clients in finance, retail, agriculture and more. My role required me to be client-facing and agile to our clients’ needs.— internal fraud investigation and remediation projects for one of KPMG NZ’s largest finance clients— developed a machine learning model for one of NZ’s largest vehicle dealerships. Attained better results than industry leaders in AU/NZ— identified $5M+ in lost revenue above and beyond expectations for one of New Zealand's largest construction companies

Developed a machine learning model/pipeline to detect energy theft. The model was built in R and explored several novel concepts: irregular time series, cluster analysis and machine learning.

Tested and evaluated the OpenAPI framework for R. I communicated findings to the core team weekly. Later in the project I converted the framework from R to Python. We primarily worked with open data, web data and GIS.

Coordinated a team of four in surveying concertgoers. With the data retrieved from the surveys, I was responsible for collating and analysing data for management.