Welcome to my digital workspace. I’m Javier, a data strategist with a passion for transforming raw data into actionable insights that fuel business growth and operational excellence. Whether it’s designing data governance frameworks or forging powerful strategic alliances, I’m driven by the impact that informed decision-making can have on organisations.
On this site, I share key projects, insights, and reflections from my ongoing journey in data analytics and leadership.
Master of Artificial Intelligence (Ongoing), 2026
Victoria University of Wellington
Master of Fintech & Investment Management (Ongoing), 2025
Lincoln University
Data Science Specialisation, 2018
The John Hopkins University Online
BSc in Life Sciences, 2017
National University of Singapore
Led the Health and Digital Monitor and CXI Monitor surveys for MBIE’s Better for Business programme, providing key insights to inform government strategies and improve support for New Zealand businesses.
Developed and implemented a data strategy for Kiwibank, enhancing their data capabilities and strategic insights.
To help remember which bins to put out on the street during garbage pickup day.
Utilised data to identify and support businesses affected by Cyclone Gabrielle with a $50 million relief package.
Implemented strategies to reduce assault cases in MSD service centres by analysing and integrating multiple data sources.
Leveraged advanced machine learning techniques to develop a predictive modelling pipeline at the National University Hospital Cardiovascular Unit, enhancing patient outcome predictions by 30%.
Revamped MSD’s data management and business intelligence practices, leading to improved data handling and quality.
Evaluated and implemented dashboard technologies for the Reserve Bank of New Zealand, enhancing reporting capabilities.
The tool uses a woman’s own personal information to estimate risk of developing invasive breast cancer over specific periods of time
The main goal of this capstone project is to build a shiny application with the ability to predict the next word.
At the NUHS MIT Healthcare Datathon 2018, a predictive model using XGBoost and Random Forest was created to identify factors influencing post-operative outcomes and guide surgical management for elderly patients.