Building interpretable, quantitative risk intelligence at the intersection of statistics, AI, and complex systems.
- Statistics student and Junior Data Analyst focused on Quantitative Risk, Fraud Analytics, and AI-driven optimization.
- Experienced in transforming statistical theory, machine learning, and optimization models into real-world decision-support systems.
- Strong interest in financial markets, logistics risk, systemic stress modeling, and explainable AI.
- Currently a 3rd-year Statistics undergraduate, actively seeking internship and junior-level opportunities in data and risk analytics.
- ⚛ EconSSI — Systemic Stress Intelligence
- 🚚 TÜBİTAK 2209-A — AI-Based Safe Cargo Optimization
- 💸 Explainable Financial Anomaly Intelligence
- 🏦 WDI–EKC Sustainability Framework
- 🤖 Insider AI Weekend — LLM Studies
- ⛏️ Statistical Modeling of Thorium-232 Transformation
- 📑 AI Group 76 Project
Python · Pandas · NumPy · Scikit-Learn · Statistical Modeling (Regression, Inference) · Feature Engineering
Quantitative Risk Analysis · Fraud & Anomaly Detection · Optimization Modeling · Time Series Analysis · Systemic Risk
SQL (Analytical Queries) · Exploratory Data Analysis (EDA) · Data Cleaning & Validation · Matplotlib · Risk-Oriented Visualization · Model Interpretability (SHAP)
Git & GitHub (Version Control) · Jupyter / Google Colab · Experiment Tracking · Reproducible Research · Structured Notebooks & Reporting