Description

Data Analyst with a strong foundation in mathematics and statistics (Agrégation).

I specialize in exploratory data analysis, statistical modelling and transforming complex datasets into actionable insights using Python and SQL.

My background in mathematics (Agrégation) allows me to design rigorous analyses, build statistical models, and interpret results with strong methodological reliability.

Experience working with environmental and biological datasets, including biodiversity data from APIs such as GBIF and GeoFabrik. Through the BioDivMap project, I developed a predictive model to identify potential observation areas of the Eurasian lynx and produced geospatial analyses to support ecological monitoring.

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent
German
Conversational

Workplace preferences

Can work on-site

Bâle (up to 30km), Mulhouse (up to 10km), Colmar (up to 10km), Strasbourg (up to 10km), Fribourg-en-Brisgau (up to 10km)

DataForGood
Developer for EuroforDocs
CIVIC AND SOCIAL ORGANIZATIONS
January 2026 - Today (5 months)
Integrated new external datasets (RNA registry) to enrich and improve the completeness of the organization database Performed data cleaning, normalization and transformation using Python and SQL Conducted exploratory data analysis (EDA) to identify inconsistencies and improve data quality Collaborated with a multidisciplinary team using Agile/Kanban methodology and GitLab version control.

Gitlab repository :
Data analysis Data Quality ETL Python SQL
Maxime Coffre
Data sciencs project: Biodivmap
ENVIRONMENTAL
February 2026 - March 2026 (1 month)
Huningue, France
Design of a complete data pipeline to produce a probability map of lynx presence across Europe.
Collection and structuring of massive environmental data via Google Earth Engine, geospatial processing with Python (GeoPandas, raster).
Feature engineering and training of a machine learning model (Logistical regression) to model the presence of a species despite observation biases between countries.
Development of a spatial decision support tool to identify areas with high observation potential.
SIG Scikit-learn Feature Engineering Machine learning Python
DataBird Paris
Data Analyst : Biomedical Project
HEALTH AND WELLNESS
November 2025 - December 2025 (1 month)
Huningue, France
Cleaned and processed a 10,000-row biomedical dataset using Python (Pandas) Performed exploratory analysis to identify factors associated with antibiotic resistance.
Presented findings through data visualization and statistical summaries.
Detected 27 relevant correlations between risk factor and bacterial resistance.
Python Data visualisation Life Sciences Tests statistiques PowerBI