Freelancer profile translated to English.

Description

Expert in Python, SQL, and machine learning, I transform complex data into precise insights and operational solutions. Rigorous, analytical, and business-oriented, I deliver reliable, well-explained, and actionable results. Curious and adaptable, I continuously progress to bring concrete impact.

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent
Arabic
Native or bilingual

Workplace preferences

Can work on-site

Paris (up to 50km)

JULES
Data Scientist
FASHION AND COSMETICS
January 2020 - Today (6 years and 5 months)
Roubaix, France
Data Team (20 people), using agile/Scrum methodology, in collaboration with the team

Project “Pricing: development of a machine learning model to define the fair price of a garment based on transactional data”
• Collection, cleaning, and structuring of multi-source data (Python, SQL, Snowflake, BigQuery, Cloud Storage, Excel, CSV) to build reliable and usable datasets for analysis and modeling.
• Descriptive and exploratory analysis to identify key trends and variables influencing sales.
• Creation and selection of new features, with analysis of their importance to evaluate their impact on predictive performance.
• Comparison and testing of several pricing models, selecting the best performing one.
• Development of a predictive model to estimate seasonal sales profiles and refine forecasts.
• Development of a pricing model based on product attractiveness, integrating estimation of demand decay, elasticity calculation, clustering, and sales simulation to recommend the optimal price.
• Development of Streamlit applications for the sales profile prediction model and the pricing model.
• Deployment of applications on Cloud Run and Snowflake.
Ad-hoc analyses:
• Price strategy analysis (impact of new prices on sales)
• Sales performance analysis by customer segment (age, gender, tenure, …)
• Analysis of sales predictions made by external providers (comparison with actual sales)
Technical environment: Python, Pandas, SQL, Snowflake, Streamlit, GCP, BigQuery, Vertex AI, Cloud Run, Cloud Storage, NumPy, Matplotlib, Plotly, Scikit-learn, XGBoost, LightGBM, K-Means
Snowflake Google Cloud Platform (GCP) Streamlit Python SQL
DELTA FOODS
Data Scientist
AGRICULTURE
January 2020 - August 2023 (3 years and 7 months)
Paris, France
• Collection, cleaning, and structuring of multi-source data.
• Normalization and preparation of reliable datasets for modeling (Python, Pandas, SQL).
• Development and automation of a complete data processing pipeline (Python + SQL + Talend). Data analysis:
• Sales analysis: seasonality, market trends, product/range performance, customer segmentation.
• Analysis of raw material costs, margins, and turnover to optimize commercial performance and profitability.
• Construction and automation of reporting: daily, weekly, monthly, and annual.
• Creation of Power BI and Excel dashboards for operational management.
• Product/customer segmentation (clustering) and analysis of purchasing behaviors. Machine learning:
• Development of predictive models to forecast sales and anticipate stock levels, optimizing production planning and reducing stockouts and overstocks.
• Development of a pricing model integrating raw material costs, seasonality, and market trends to recommend optimized prices.
• Model optimization to improve forecast and recommendation accuracy. Technical environment: Python, Pandas, NumPy, SQL, Scikit-learn, Prophet, ARIMA, LightGBM, Talend, Power BI, SQL Server, Excel.
Pandas Talend Microsoft Power BI Microsoft Excel Python
Esky web
Data Developer / Django
DIGITAL AND IT
January 2019 - December 2019 (11 months)
Tunis, Tunisia
• Modeling, structuring, and optimizing PostgreSQL databases used by internal applications.
• Development of back-end interfaces, enabling reliable data exposure, filtering, and aggregation for users and internal dashboards.
• Writing SQL queries for operational data extraction, analysis, and reporting.
• Development of Python scripts to automate repetitive tasks (data cleaning, table updates, integrity checks), reducing manual workload for teams.
• Contribution to improving data quality: anomaly correction, integrity rule verification, structure normalization, and performance optimization.
• Collaboration with the project team to transform business needs into efficient data models, ensuring the consistency and performance of critical queries.
• Development of internal tools for simple consultation and visualization (tables, Excel/CSV exports) to help teams better leverage data. Technical environment: Python, Django, SQL, PostgreSQL, HTML/CSS, JavaScript (jQuery).
Python Django SQL JavaScript HTML/CSS