Digital Economics & Quantitative Analytics | Software Engineering
BSc in Digital Economics and Business (English Curriculum), Università Politecnica delle Marche
Ancona, Italy · Open to international opportunities & quantitative engineering (Netherlands / EU)

LinkedIn / Email / GitHub

Undergraduate in Digital Economics and Business with a technical background in software engineering, systems architecture, and relational databases. Focused on bridging quantitative economics, corporate finance, and modern data infrastructure.

Available for freelance analytics engagements and 2026/2027 international traineeships.

• Quantitative Finance & Econometrics: Portfolio optimization (Markowitz), Monte Carlo risk modeling (VaR, Drawdown), time-series analysis, multiple linear regression, statistical inference.
• Data Engineering & Systems: Automated ETL pipelines, relational database architecture, Docker containerization, workflow orchestration.
• Languages & Frameworks: Python (NumPy, SciPy, Pandas, Scikit-Learn), R (Tidyverse, Survival), SQL (PostgreSQL), Bash, Linux (WSL/Ubuntu).
• Corporate & Business Analytics: Working capital analysis, cash flow forecasting, unit economics, executive reporting dashboards.

Automated market data ingestion into PostgreSQL with zero-duplicate upserts, Markowitz Mean-Variance portfolio optimization for Maximum Sharpe Ratio, and 10,000-path Monte Carlo simulations for Value-at-Risk forecasting. Containerized with Docker Compose and scheduled via Apache Airflow DAGs.

• Python PostgreSQL Apache Airflow Docker Compose SciPy

Predictive clinical deterioration models (AUC = 0.902) using Kaplan-Meier survival analysis and Decision Curve Analysis (DCA). Includes digital signal processing on 100 Hz PPG biosignals and a 1,000-iteration Markov decision model demonstrating cost savings in intensive care environments.

• R (Tidyverse, survival) Biostatistics DSP HEOR

Algorithmic trading framework based on Marcos López de Prado's Advances in Financial Machine Learning. Implements fractional differentiation for stationary time-series with maximum memory retention, Lempel-Ziv entropy regime detection, and triple-barrier risk controls.

• Python MetaTrader 5 NumPy SciPy

Normalized enterprise schemas for transactional records, billing systems, and accounting data. Tuned for query execution efficiency using window functions, common table expressions, and targeted indexing strategies.

• SQL PostgreSQL Database Modeling Query Optimization

• Automated Financial Dashboards: Building automated data pipelines that replace manual spreadsheet workflows with unified cash flow, margin, and working capital visibility.
• Econometric & Causal Modeling: Estimating demand elasticity, revenue drivers, and customer cohort retention through rigorous statistical testing.
• Transactional Data Normalization: Auditing, cleaning, and structuring multi-source accounting and operational data for reliable managerial decision-making.

• BSc in Digital Economics and Business, Università Politecnica delle Marche (2024 – 2027)
• Diploma in IT & Telecommunications, ITI Enrico Mattei
• Focus: CFA Level 1 Preparation (Quantitative Methods, Financial Statement Analysis, Corporate Issuers)

• Email: harounjaafar3@gmail.com / haroun.jaafar@studenti.univpm.it
• LinkedIn: linkedin.com/in/haroun-jaafar