Section 01 / 04 · About
I work at the seam between data, design and the decisions they support.
The through-line is story-led analytics. Models that inform a decision, dashboards that leaders actually use, AI implementations placed where they shorten the path from question to answer.
Based in Melbourne, working in financial services by day, and on home-lab experiments and design problems most other evenings.
01 The arc
- Early Payroll, healthcare and retail Hospital and fashion-retail payroll, where the discipline of getting data right under audit pressure was first learned.
- 2014 Business banking A first move into financial services, learning customer outcomes from the front line.
- 2015 Resource planning Forecasting and workforce planning, the first formal home for statistical thinking applied to a real business.
- 2017 Design, in parallel A formal grounding in graphic design and a freelance practice alongside the day job, a discipline that still shapes how analytics is presented today.
- 2018 Operational analytics Power BI, Tableau, and the move toward analytics as the language for operational decisions.
- 2022 Data science and people analytics Capability assessment, skills taxonomies, learner segmentation, NLP on workforce feedback. Where analytics started carrying more weight in strategic conversations.
- 2024 Graduate Certificate in Data Science (RMIT) Formalising the practitioner habits and extending into applied analytics, regression, and statistical communication.
- 2025 Master of Data Science (RMIT) Twelve-subject curriculum spanning consumer analytics, ML for decision-makers, data architecture, AI ethics, digital innovation, leadership under disruption.
- 2026 ML, AI, and what comes next Leadership reporting, automation, internal tooling, and AI integrated into production pipelines and personal workflows. Less about chasing the model, more about rationalising where AI genuinely shortens the path from question to answer.
02 How I work
Confidence through clarity
Fewer reports that get used. One source of truth per metric. Data lineage legible enough that a question about source data has a fast, findable answer.
Decisions over reconciliation
Leadership meetings spend time on judgements, not on "what is the right number." Pre-reads replace live walk-throughs where possible.
Reusable capability
Every delivery leaves tooling another analyst could pick up. Patterns get captured in context, not in heads.
Constraint as filter
Five-row update tables, three-bullet condensed versions. The limit forces synthesis, which is where the real translation happens.
03 Beyond the day job
Personal AI agents
I run a workforce of four named AI personas on a small Linux server at home. Samwise sweeps Gmail, Faramir watches the budget, Gwaihir audits security, Gandalf keeps the rest of the house honest. Each one has their own job, their own voice, and their own audit log. The system is where I learn what production-grade AI integration feels like, before bringing those lessons into work.
A personal server
A small Linux machine at home that I treat the way a professional team treats production. Scheduled jobs, version control, real logs, recovery procedures. It is where I practise the operational discipline that the day job depends on.
Visual craft
A formal grounding in graphic design and a freelance practice, both visible in the typography, layout discipline and design sense applied to analytics outputs today.
Off-screen
Science fiction, film, cycling, the gym. The unhurried half of the week that keeps the other half honest.
04 Formal study
- 2025
- Master of Data Science · RMIT University
- 2024
- Graduate Certificate in Data Science · RMIT University