Singapore
Aspiring banker turned AI/ML enthusiast with an affinity for numbers, analytics, and problem-solving. I thrive in fast-paced, high-challenge environments where curiosity and adaptability matter. I initially pursued a path in finance, spending much of my early years working toward a career in investment banking. However, an internship at a stock exchange opened my eyes to how rapidly technology was reshaping the industry. I found myself trading spreadsheets for models and code, and haven’t looked back since. Today, I work at the intersection of AI and cybersecurity, building smarter systems, uncovering insights, and thinking about how to make technology not just intelligent, but secure and resilient. In my free time, I am always on the lookout for the next adrenaline filled adventure. I enjoy jumping off planes, diving the depths of the ocean and everything else in between!
• Spearheaded development of a production-grade product lifecycle prediction engine achieving <3% error, enabling data-driven product strategy and shortening R&D timelines by 3–6 months • Modernized data and ML infrastructure by redesigning schemas and ingestion workflows and migrating pipelines to GCP (BigQuery, GCS, Vertex AI Pipelines), eliminating manual intervention while improving scalability, reliability, and reproducibility
• Enhanced internal NLP capabilities by developing production tools for Named Entity Recognition (NER) and text classification, improving automation and downstream analytics • Improved time-series forecasting models to predict hourly media content views, increasing traffic forecasting accuracy and supporting data-driven content strategy • Built demographic prediction models (age, gender, ethnicity) using clickstream data, enabling targeted personalization and audience segmentation • Designed and deployed an end-to-end Device Graph pipeline, leveraging graph theory and machine learning to probabilistically link devices and improve cross-device identity resolution • Co-authored research on the Device Graph solution accepted at the FLAIRS 2021 Conference, recognized for its novel methodology and strong commercial applicability
• Awarded scholarship to pursue Master’s Degree
Created digital course materials for an Introduction to Python Module
Postgraduate (6-month) Internship: • Worked closely with Data Scientists on the research and development of Device Graphs to build a scalable and commercially viable product • Developed pipelines for model performance monitoring to ensure performance drifts can be detected swiftly • Built Demographic(age, gender, ethnicity) prediction models based on click stream data