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Data Scientist with 5+ years in Predictive Modeling & Data Engineering
PhD-trained data scientist with five years of experience building predictive models, identifying patterns in complex datasets, and communicating findings clearly to non-technical stakeholders. Track record of delivering end-to-end analytical solutions from raw data through to documented, actionable outputs. Strong quantitative foundations in statistical modelling and machine learning, with genuine curiosity about how data can drive product and operational decisions. Making the move into industry to work on problems with real-world impact.
Griffith University
PhD in Physics · Physics
August 1, 2021 – June 30, 2025
Mahidol University
Bachelor of Science · Physics
August 1, 2016 – June 30, 2020
Griffith University
Research Assistant
January 1, 2025 – Present
Queensland, Australia
Griffith University
PhD Researcher
January 1, 2021 – January 1, 2025
Queensland, Australia
NLP Text Analysis Pipeline
June 1, 2026 – June 1, 2026
Built an NLP pipeline on 251 songs collected via Genius API; applied TF-IDF vectorisation, cosine similarity, and vocabulary richness metrics to identify longitudinal patterns across 12 albums. Translated technical findings into a clear public blog post demonstrating the ability to communicate data insights accessibly to non-technical audiences.
View ProjectHousing Price Prediction Model
May 1, 2026 – May 1, 2026
Designed and built an end-to-end predictive modelling pipeline on 20,640 real-world records, comparing Linear Regression, Random Forest, and XGBoost with full hyper-parameter tuning and 5-fold cross-validation; best model achieved R2 = 0.82. Conducted systematic residual and geographic error analysis to identify structural model failures; engineered coastline and city distance features via KD-tree spatial lookup, lifting XGBoost R2 from 0.80 to 0.82. Full pipeline from raw data through cleaning, feature engineering, model selection, and evaluation; findings published as a public blog post for non-specialist audiences.
View ProjectSequential State Estimation Framework
January 1, 2025 – January 1, 2026
Developed and benchmarked five numerical estimation algorithms across 5,000 Monte Carlo realisations; engineered a time-weighted feature statistic reducing mean error by an order of magnitude versus baseline. Reproducible pipeline with automated validation via GitHub Actions and full structured documentation.
View ProjectData Analytics Professional Certificate
DeepLearning.AI
June 1, 2026 – Present
Supervised Machine Learning: Regression and Classification
DeepLearning.AI
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's background in academic research, particularly a PhD in Physics, suggests a strong drive for rigorous analysis and problem-solving. Their personal projects demonstrate initiative and a proactive approach to learning and applying data science techniques. The emphasis on reproducible pipelines and clear communication aligns with best practices in collaborative environments. The candidate's stated desire to work on real-world problems indicates a practical orientation, which is a good cultural fit for industry roles. However, the experience is primarily academic, and a direct transition to a fast-paced industry environment might require some adjustment.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving skills, honed through PhD research. Their experience in collaborating with cross-functional teams and presenting findings at conferences suggests good teamwork and presentation abilities. The ability to translate technical findings into accessible blog posts indicates strong communication skills. The candidate's interest in moving into industry for real-world impact aligns well with an operational role requiring practical application of data science.