Freelancer profile translated to English.
Back to original languageAbout Ibrahim
Data Scientist with expertise spanning the entire project lifecycle: data exploration and cleaning, statistical and machine learning modeling, visualization, through to production deployment.
I work on three main types of projects:
- Prediction & Classification : prices, churn, real estate — delivering a deployed and documented model
- Segmentation & Clustering : customer segmentation, pricing optimization ...
- Analysis & Reporting : HR, e-commerce, or economic data exploration, with clear decision-oriented visualizations
My approach is rigorous and results-oriented: I deliver complete projects, from the first line of code to the user interface (Streamlit) or production API.
I also work on any data project in Python requiring analytical or predictive expertise: automation, multi-source data processing, pipelines, ad hoc studies.
Deliverables :
- Documented and deployed ML models (API or Streamlit application)
- Clear analysis reports with actionable recommendations
- Interactive dashboards and business visualizations
- Clean, GitHub-versioned, reusable code
Main Stack : Python (Pandas, Scikit-learn, Plotly, Polars), advanced SQL, Streamlit, Git/GitHub, deployment, Linux environment, Machine Learning, Clustering Customer Segmentation , Streamlit, Plotly NLP Git , Feature Engineering
French
Native or bilingual
Can work on-site
Strasbourg (up to 50km), Mulhouse (up to 10km), Paris (up to 10km)
Experience
- INRAEData ScientistREAL ESTATEMarch 2024 - August 2024 (5 months)Avignon, FranceAnalysis of the real estate market in the Vaucluse departmentTasks performed:
- Data exploration from various sources (DVF+, BPE, BD TOPO, transport.data.gouv.fr).
- Data processing, cleaning, and preparation.
- Advanced statistical analyses (trends, correlations, statistical tests, etc.).
- Data visualization and spatial representation (QGIS).
- Developing predictive statistical models to estimate prices (Linear Regression, Xgbbost, Random Forest).
- Collaboration with business teams to translate needs into actionable indicators.
- Design of dashboards and automated reports for monitoring business indicators.
Presentation:- Presenting results to researchers.
- Creating a synthetic dashboard.
Skills acquired:- Data Engineering
- Statistical Analysis
- Spatial Analysis
- Development and optimization of predictive models
Communication- Clear and pedagogical presentation
- Report writing
Soft skills:Autonomy, rigor, teamwork, curiosity, and learning ability. - Université de RennesData AnalystENVIRONMENTALJune 2023 - September 2023 (3 months)Rennes, FranceAnalysis of energy poverty in France
- Collection of data from institutional and governmental sources.
- Data cleaning, processing, and feature engineering.
- Univariate and bivariate statistical analyses.
- Principal Component Analysis (PCA).
- Development of machine learning models to explain and analyze energy poverty.
- Presenting and explaining results to interdisciplinary teams
Tools used: R and Python, with libraries for data processing and visualization (dplyr, ggplot2), machine learning (caret), and clustering (k-means).
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Education
- Data Science & AI Training – MLProMachine Learnia – MLPro2026Formation intensive en Data Science — des fondamentaux à l'état de l'art Module 1 – Les 6 piliers de l'IA (Python, SQL, Git, travail collaboratif, terminal Linux (Bash, Poetry, uv, pyenv), frameworks de Data Science) Module 2 – Mathématiques (Algèbre linéaire, statistiques et probabilités, analyse et calcul mathématique) Module 3 – Machine Learning (Modèles linéaires, arbres de décision, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), méthodes ensemblistes, clustering, réduction de dimension) Module 4 – Deep Learning (ANN, CNN, RNN, Transformers, YOLO, segmentation d'images...) Module 5 – MLOps (FastAPI, Docker, MLflow, Evidently AI) Module 6 – Data Engineering (PostgreSQL, dbt, Apache Airflow, Apache Spark...) Module 7 – Cloud (AWS, GCP, Azure, services de Machine Learning...) Module 8 – Large Language Models (LLMs), Agentic AI et génération d'images Module 9 – Apprentissage par renforcement
- Master of Applied Mathematics and Statistics, Data Science and Econometrics trackUniversité de Rennes2024Statistiques, économétrie, Data Management, Data Visualisation, Analyse de données, Machine learning, Deep learning, IA, NLP