Data Scientist & Analytics Engineer
MS Analytics @ Georgia Tech · ML · Statistical Modeling · Data Engineering
View My WorkAbout Me
I build ML systems end-to-end, from raw data to working model. My first year at Georgia Tech's MS Analytics program produced three deployed projects spanning churn prediction, computer vision, and unsupervised fraud detection. I'm carrying a 4.0.
My background combines computer science, mathematics, and finance. I can write the query, build the model, and understand what the output actually means for a business.
That combination doesn't come standard.
Outside of work, I follow financial markets closely and spend time on ML research. I tend to gravitate toward problems where the math is interesting and the stakes are real.
Graduate
Aug 2025 – Dec 2026
Georgia Institute of Technology
Atlanta, GA
GPA 4.0
Coursework
Undergraduate
Sep 2019 – May 2024
New York University
New York City, NY
Dean's List · Math & Finance
Coursework
Technical Toolkit
Applied Data Science
U. of Michigan
Portfolio
November 2025 · Machine Learning
End-to-end churn prediction across 7 telecom datasets (7,000+ customers). Top 10% highest-risk customers accounted for ~77% of churn. Evaluated 28 configurations; CatBoost + SMOTE was the top performer.
→ Lets telcos concentrate retention spend on the accounts most likely to leave — before they do.
October 2025 · Computer Vision
Pixel-level labeling of roads, vehicles, and pedestrians in urban driving scenes. ~62% mIoU on an 11-class dataset; ~93% mIoU on binary road segmentation via transfer learning. Compared baselines against PSPNet with dilated convolutions.
→ Foundational perception layer for autonomous vehicles, robotics, and smart city infrastructure.
September 2025 · Unsupervised Learning
Unsupervised fraud detection on 280,000+ financial transactions with no labeled data. Well-separated behavioral clusters with up to 3× improvement over random baselines. K-Means and GMM evaluated via Silhouette Score and Adjusted Rand Index.
→ Works without labeled fraud data — deployable in any financial system from day one.
TradeEasy
P2P Barter · iOS
May 2023 · Full-Stack Mobile
Mobile marketplace for item trading using a Tinder-style swipe interface. Flask + PostgreSQL backend on AWS RDS with S3 image storage. Location-aware matching via geodesic filtering, JWT auth, and real-time chat.
→ Extensible to any peer-to-peer exchange market — goods, services, or skills.
Work History
React Native · JavaScript · Git
Get In Touch
Every interesting project starts with a conversation. What are you working on?
“It always seems impossible until it’s done.”
— Nelson Mandela