Quantitative Data Scientist | Systems Modelling | Risk & Decision Analytics
I’m a quantitative data science student focused on modelling complex systems. Most of my work involves extracting structural patterns from large, messy datasets — whether that’s macroeconomic forecasting, disaster resilience modelling, or AI-driven systems. I’m particularly interested in applying rigorous statistical methods to high-impact domains like finance, healthcare, and strategic analytics.
- Relevant Coursework: Machine Learning, Deep Learning, Data Mining, Big Data Systems, Database Systems & Warehousing, Advanced Statistics, Operating Systems, Software Engineering.
- Programming Languages: Python (Pandas, NumPy, Scikit-learn, Flask), R, C++, SQL (PostgreSQL, MySQL, SQLite), x86 Assembly.
- Statistical & Quantitative Modelling: Time-Series Forecasting (ARIMA), Regularised Regression (Ridge, Lasso, Elastic Net), Hypothesis Testing, Feature Engineering, Model Diagnostics, Econometric Analysis.
- Machine Learning & AI: Supervised Learning, Multi-label Classification, Deep Learning (CNNs, RNNs), NLP, Hugging Face Transformers, RAG Systems, LangChain, LangGraph
- Data Engineering & Big Data: Data Cleaning, Missing Data Imputation, Outlier Detection, Multi-source Data Fusion, Vector Databases (Pinecone), PostgreSQL.
- Visualisation & Analytics: High-Dimensional Visual Analytics, Parallel Coordinates, Choropleths, Correlation & Density Analysis.
- Systems & Tools: Git, GitHub, Linux(Arch, Mint, Ubuntu), Windows, REST APIs.
- Interests: Quantitative Systems Thinking, Economic Modelling, Tech Leadership, Digital Marketing Analytics.
- Languages: English (Fluent), Urdu (Native), Italian (Basic)
Connect with me on LinkedIn
My Resume
ibrahimbeaconarion@gmail.com