About Me
I’m a recent graduate of Columbia University with a Master’s in Applied Analytics, and I hold a Bachelor’s degree in Electrical & Computer Engineering from Brunel University London. My passion lies in solving real-world problems through data. Over the past few years, I’ve honed my skills in data analysis, machine learning, and backend development to build intelligent systems that deliver business value.
My journey began with web development—learning HTML, CSS, and JavaScript—but quickly evolved into building more dynamic and data-driven applications using React, Next.js, and serverless technologies. Alongside this, I developed a deep interest in data engineering and analytics workflows, working on projects involving APIs, automation, and natural language processing.
At Columbia, I focused on mastering statistical modeling, predictive analytics, and data storytelling. I’ve built machine learning models for financial fraud detection, performed sentiment analysis on social media data, and automated multi-platform workflows using cloud-based tools. One of my key projects, Tweel, is a sentiment analysis tool designed to surface emotional context behind trending Twitter topics—giving users a clearer view of global discourse.
I’m especially drawn to roles where I can bridge the gap between raw data and business strategy—turning numbers into actionable insights. I believe the real challenge isn’t just solving a problem, but defining it well.
Outside of work, I love reading, playing tennis and chess, running, and exploring NYC’s food and coffee scene.
Tech Stack
