01
Problem
Led the Data Science team for the Melbourne Open Playground (MOP) project, coordinating the review, standardisation, and publication-readiness of data science use cases across a repository of 200+ use cases.
02
Method
- Established consistent Jupyter Notebook standards, developing the first reference implementation used to guide consistent delivery across future Data Science work.
- Built and implemented a centralised work allocation and tracking process, improving visibility of ownership, workload and delivery progress across the team.
- Led technical reviews across code, analytical notebooks, data visualisations and documentation, providing actionable feedback and driving quality improvements before release.
- Mentored team members on Git/GitHub, pull request workflows, code quality and technical troubleshooting, supporting issues through to resolution.
- Worked cross-functionally with Web Development and Quality Management teams to translate requirements, resolve technical dependencies and coordinate deliverables.
- Improved repository and delivery workflows by introducing clearer release structures and processes for managing reviewed and completed work.
03
Results
- Delivered hands-on Data Science solutions alongside leadership responsibilities, taking multiple analytics use cases from review through to production-ready project outputs.
- Ranked as the #1 repository contributor with 64 commits during the project period.
