Pooja Ramesh
Data Analyst
Dublin, Ireland
Summary
🤩
Rockstar🎓
Top SchoolPooja Ramesh is a Data Analyst with a Master’s in Data Analytics from Dublin City University and over a decade of hands-on experience translating transactional and real-time finance data into actionable business insights. She has delivered analytics and visualization solutions across financial services and transport—most recently at Iarnród Éireann Irish Rail—and previously built anomaly detection and alert analytics using Python, Weka, ELK and Kibana at VuNet and Dataminr. Early work at TCS exposed her to cloud automation and enterprise tooling, while her open-source contributions to a Clojure ML repo demonstrate practical ML engineering skills such as pooling-layer tests and a network graph API for model manipulation. Comfortable with large-scale data cleaning, association mining and REST-driven location analytics, she blends strong technical fluency with client-facing delivery experience. Colleagues describe her as curious and fast-learning, eager to apply ML concepts to improve customer outcomes in fast-paced environments.
10 years of coding experience
5 years of employment as a software developer
Bachelor’s Degree, Computer Science, First class with Distinction, Bachelor’s Degree, Computer Science, First class with Distinction at Sri Sairam Engineering CollegeMaster's degree, Data Analytics, 1.1 - First Class With Honours, Master's degree, Data Analytics, 1.1 - First Class With Honours at Dublin City University
English, Tamil
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
clojure10
neural-network10
machine-learning10
convolutional-neural-networks10
clojure-cli10
testing9
deep-learning9
Programming languages (2)
Github contributions (5)
originrose/cortex
Feb 2016 - Feb 2017
Machine learning in Clojure
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 3 PRs, 5 pushes in 1 year
Contributions summary:Pooja made several contributions related to machine learning model testing and network API enhancements within the Clojure-based machine learning repository. They added tests for pooling layers, focusing on scenarios with negative inputs and edge cases. Furthermore, they implemented a network graph API, enabling modification of the network structure, including adding and removing layers. This included fixing bugs, updating buffer shapes, and creating new methods for network manipulation and evaluation.
clojuremachine-learning
poojaramesh/capstone
Jan 2016 - Jan 2016
Contributions:7 commits, 8 pushes, 1 branch in 12 days
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