Data Science student at Alexandria University building ML pipelines, uncovering insights from data, and learning something new every day. Currently seeking internships and research opportunities.
Who I Am
I'm Abdelrahman Eslam Omar, a first-year Data Science student at Alexandria University (Class of 2028) with a real passion for turning messy data into meaningful insights.
I've built ML pipelines that achieved 96% R² on prediction tasks, led a 10-person team on a regression project, and spent countless hours analyzing over a century of Nobel Prize data — all because I genuinely find this stuff fascinating.
Currently training as a Data Analyst at Skills Dynamics (DEPI), I'm actively seeking internships and research collaborations where I can contribute and grow alongside experienced teams.
Based in Alexandria, Egypt. Open to remote opportunities worldwide.
Building predictive models and understanding the math behind them — from scratch when possible.
Turning raw datasets into actionable insights through rigorous EDA and visualization.
Reading papers, replicating experiments, and pushing my understanding deeper every week.
Building end-to-end ML systems, from raw data to deployed models.
What I Work With
Programming
Data Science
Machine Learning
Tools & Workflow
Soft Skills
Mathematics
📚 Currently Learning
Academic Background
Work History
What I've Built
From analysis notebooks to full ML pipelines — here's what I've built so far.
Curious About
Areas I'm actively studying, planning to explore, or hoping to contribute to someday.
Understanding how machines process, interpret, and generate human language. Interested in transformers, text classification, and language modeling.
Studying the architecture and capabilities of foundation models. Curious about fine-tuning, prompt engineering, and efficient inference.
Building neural networks from scratch. Focused on backpropagation, CNNs, RNNs, and the attention mechanism.
Teaching machines to see and interpret visual data. Interested in object detection, image segmentation, and visual transformers.
Exploring how agents learn optimal behavior through environment interaction. Q-learning and policy gradient methods.
Building robust, scalable ML systems. Learning about model deployment, monitoring, data pipelines, and reproducibility.
If you're working on a research project in any of these areas and need a motivated contributor, I'd love to hear from you.
Get In TouchWriting
I write about machine learning, data science, and lessons learned from projects.
Let's Connect
Whether it's an internship, research collaboration, or just a chat about AI — I'd love to hear from you.
I'm actively looking for data science and AI internships, research positions, and open-source collaborations. I usually respond within 24 hours.