Open to work · Brisbane, Australia
Hi, I'm Charlie Rego — data scientist
turning messy data into clear decisions.
MDS candidate at QUT (graduating July 2026). Background in financial audit at EY, hands-on internship at a PropTech startup. I build models, pipelines, and the occasional website.
About me
I came to data science from an unlikely direction — three years auditing listed companies at EY India. Audit sounds dry, but it taught me something that most data courses don't: how to be rigorous about data quality, and how to translate numbers into decisions that real people act on.
That foundation pushed me toward the technical side. I moved to Brisbane in 2024 to do a Master of Data Science at QUT, and picked up an internship at Occubuy — a property tech startup — where I got to do actual embedded DS work: customer segmentation, MongoDB pipeline optimisation, Google Analytics funnel analysis that directly changed product decisions.
I'm most comfortable in Python — pandas, sklearn, PyTorch — and SQL. I'm also building out my web skills, which is how this site happened. The AFL predictions project is my current obsession: an end-to-end ML pipeline that forecasts player performance, deployed as an interactive dashboard you can explore above.
I'm looking for data scientist, analyst, or ML engineer roles in Australia. I'm on a student visa, graduating July 2026, and I'm eligible to work on a post-study work visa after that.
Projects
A mix of academic, industry, and personal work.
AFL Player Performance Prediction
In progressEnd-to-end ML pipeline predicting AFL player disposals, marks, and goals using historical stats and current season data. Features rolling averages, position encoding, and an ensemble model (random forest / gradient boosting). Predictions served as static JSON and visualised in an interactive dashboard.
AI-Assisted Land Assessment System
CompleteBuilt for the Queensland Dept. of Natural Resources as an industry capstone at QUT. A multi-stage AI orchestration pipeline supporting automated information extraction and explainable decision-support for government officers. Full-stack prototype with React, RESTful API, and Docker deployment.
Customer Segmentation — Occubuy
CompleteCleaned and prepared property platform data (missing values, cross-attribute imputation), then applied clustering algorithms to identify distinct customer segments. Findings directly informed product strategy and targeted marketing campaigns.
charlierego.com
LiveThis site — built from scratch as a portfolio piece alongside the MDS degree. Next.js App Router, Tailwind CSS, Firebase Auth + Firestore for real-time messaging, deployed on Vercel. The chat widget lets recruiters message me directly without email friction.
Skills
Languages
- Python
- R
- SQL
- TypeScript
- C#
- Java
ML & Data Science
- Supervised & Unsupervised Learning
- Deep Learning / Neural Networks
- NLP & Text Mining
- Clustering & Segmentation
- Stochastic Modelling
- ML at Scale
Data & Analytics
- pandas · numpy · matplotlib
- scikit-learn · PyTorch
- MongoDB · NoSQL
- Google Analytics
- Jupyter Notebooks
- SAP · Microsoft Excel
Engineering & Web
- Next.js · React
- Tailwind CSS
- Firebase (Auth + Firestore)
- Docker
- Git / GitHub
- REST APIs
Experience
Nov 2025 – Mar 2026
Brisbane, AU
Data Science Intern
· Occubuy (PropTech)- Customer segmentation using clustering on survey + database records — directly informed product strategy.
- MongoDB indexing and pipeline engineering to consolidate multi-source data for downstream analytics.
- Google Analytics funnel analysis revealing signup drop-offs and peak engagement windows; findings adopted by product team.
Feb 2018 – Feb 2021
Bangalore, India
Article Intern — TMT Assurance
· EY India (Big Four)- Audited listed and multinational clients — rigorous data verification across complex financial datasets.
- Worked with SAP and MIS platforms to extract, transform, and interpret structured business data.
- Built the data discipline and structured problem-solving that now underpins my approach to ML and analytics.