Freelancer profile translated to English.

Description

I am a Data Analyst specialized in risk and econometrics, a graduate engineer from INSEA, with a strong background in statistics, modeling, and applied data science. I assist companies, institutions, and project leaders in leveraging their data to produce reliable, clear, and directly actionable analyses for decision-making.

My experience covers concrete projects in finance, risk analysis, public policy, and data analytics, ranging from data preparation and structuring to advanced modeling and results reporting. I have notably worked on building systemic risk indicators, developing forecasting models, credit scoring, fraud detection, as well as creating dashboards and decision-making reports.

I combine econometric rigor with modern data science tools (Python, SQL, Power BI, machine learning) to provide robust, understandable analyses tailored to business needs. I place particular importance on data quality, methodological transparency, and clear communication, including for non-technical audiences.

I can undertake missions such as: data analysis and exploration, statistical and econometric modeling, forecasting and risk analysis, reporting automation, decision-making dashboards, and ad-hoc or continuous analytical support.

Industry field of expertise

Languages

Arabic
Native or bilingual
French
Native or bilingual
English
Fluent

Workplace preferences

Remote only

Primarily works remotely

Autorité Marocaine du Marché des Capitaux
Risk Analyst
PRIVATE EQUITY
March 2025 - August 2025 (5 months)
Rabat, Morocco
I worked on monitoring systemic risk in financial markets through the construction and automation of quantitative indicators. I developed a Composite Indicator of Systemic Stress (CISS) integrating series normalization, cross-correlation calculation, and regression weighting to reflect the macroeconomic contribution of different market segments.

In parallel, I designed a financial sentiment index from economic news using NLP techniques (automated collection, text cleaning, CamemBERT embeddings). I compared the performance of market and sentiment indicators, then implemented statistical and deep learning forecasting models, achieving an average forecast error of around 5%. The results were used as decision-support tools for macro-prudential surveillance.
Deep Learning Analyse financière NLP Prévisionnel financier Etude de marché
Projet académique / appliqué
Data Analyst – Credit Scoring & Default Risk Modeling
BANKING AND INSURANCE
December 2024 - March 2025 (3 months)
Design and development of credit scoring models to assess customer default risk in the context of consumer credit. The project began with a data cleaning and structuring phase (handling missing values, encoding, normalization), followed by an in-depth exploratory analysis of repayment behaviors.

I implemented statistical and machine learning models (logistic regression, decision trees, regularized models) as well as interpretable scorecards used by business teams. The models were evaluated using standard risk indicators (AUC, Gini, KS), with a strong emphasis on interpretability and score stability.

The final deliverable includes a clear methodology, decision rules, and operational recommendations for credit risk granting and management.
Machine Learning Algorithms Data Cleaning and Preprocessing Logistic Regression Scoring Python
Projet académique / personnel
Data Analyst – Financial Time Series Forecasting (Crypto)
November 2024 - December 2024 (1 month)
Project to forecast the closing price of Solana cryptocurrency using historical market data. I collected and cleaned several years of data (prices, volumes, technical indicators), then performed an in-depth exploratory analysis to identify trends, cycles, and volatility regimes.

I developed and compared several time series models, including ARIMA and LSTM neural networks, to evaluate their predictive capability. Performance was measured using metrics such as MAE and RMSE, with a significant improvement over naive models.

The project produced actionable forecasts for financial decision support, accompanied by clear visualizations and business-oriented interpretation.
Python Deep Learning Prévisionnel financier Cryptomonnaie Time Series