Transforming complex data into evidence that drives decisions through |
I'm a data scientist and AI/ML practitioner based in Auckland, New Zealand. With a PhD in Computational Modelling and 7+ years of experience, I build end-to-end data products — from ETL pipelines and predictive models to interactive dashboards and APIs — that turn complex data into business value.
My track record includes scalable ML pipelines achieving 90% forecasting accuracy, multi-module BI platforms, and CI/CD-enabled analytics products. I've published 11 peer-reviewed papers (169+ citations) in journals like Nature Communications, demonstrating rigorous, reproducible work at the highest standard.
I thrive in cross-functional teams — translating between data science, engineering, and non-technical stakeholders to define problems, shape strategy, and deliver measurable outcomes. My domain depth in health and epidemiology is complemented by hands-on product-building experience across business analytics, clinical informatics, and AI evaluation.
I'm always interested in challenging problems at the intersection of data science, AI, and real-world impact — whether that's building intelligent products, advancing research, or driving strategy through analytics.
End-to-end ML pipelines from model development through deployment. Deep learning, NLP, ensemble methods, and predictive/prescriptive analytics.
Building dashboards, APIs, and analytics platforms. Scalable ETL, data quality monitoring, CI/CD, and reproducible workflows.
90% accuracy in trend forecasting using ensemble ML. Statistical modelling, time series analysis, and simulation at scale across 340+ regions.
Translating complex technical outputs into reports, dashboards, and presentations for business, clinical, and policy audiences.
Molecular Epidemiology and Public Health Laboratory
Fisher & Paykel Healthcare
The University of Auckland
Massey University
Inter-University Centre for Astronomy and Astrophysics (IUCAA), India
11 peer-reviewed articles • 169+ citations • Google Scholar • ORCID
Journal of the Royal Society Interface, 21(210)
Journal of the Royal Society Interface, 21(216)
Journal of the Royal Society Interface, 19(187)
Data products, ML pipelines, and open-source research code
End-to-end ML evaluation product simulating 10,000-patient cohorts to benchmark AI screening tools. Modular pipeline architecture (config/, src/, notebooks/) with stratified accuracy metrics (sensitivity, specificity, AUC-ROC).
Multi-page data product with 7 interactive modules: KPI tracking, SPC charts, anomaly alerting, automated stakeholder reporting, and statistical hypothesis testing (Mann-Whitney U, Kruskal-Wallis, Chi-Square).
Production-grade data product: 34,000+ measurements processed through a four-gate ETL pipeline with audit logging, Power BI star-schema export, interactive Streamlit dashboard, and CI/CD via GitHub Actions with pytest validation.
Large-scale predictive model across 340 regions with multi-source data integration and parameter optimisation. Open-source, reproducible pipeline accompanying first-author publication.
Simulation-based predictive modelling with parameter estimation and sensitivity analysis. Open-source research code accompanying peer-reviewed publication.
Let's build something meaningful together
I build end-to-end data products — from ETL pipelines and predictive models to dashboards and APIs. 7+ years experience, PhD-qualified, with a track record of delivering measurable outcomes across cross-functional teams.
Send Me an EmailMy expertise spans computational modelling, AI evaluation, predictive analytics, and health informatics. I'm eager to collaborate on impactful, data-driven research across domains.
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