About me
I started as an electrical and electronics engineer. My first data job was inside a microfinance bank's core-banking system. There I learned how financial data is actually created, migrated and audited, long before anyone turns it into a dashboard. That habit of checking where a number comes from has stayed with me.
From there I moved into data science: streaming pipelines at a media company, then machine-learning models for customer retention and fraud back in banking. I came to the UK for an MSc in Data Science at the University of Sussex on an AI and Data Science conversion scholarship, and graduated with a Distinction. I also worked as a research assistant, modelling climate risk to power networks.
At Moniepoint I work in Growth & Analytics. My research into what new customers do in their first weeks shaped a rebuilt onboarding journey, after which monthly active users grew 8–9% a month, up from 2–4%. I've forecast a year of transaction volume to within 1%, measured whether campaigns really worked, piloted early-warning scores for top business customers, and built the pipelines that analysis runs on.
I care about metric correctness more than speed. I'm comfortable telling a room that the data doesn't support the conclusion they hoped for, and then proposing the test that would settle it.