Beschreibung

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.

Branchenexpertise

Sprachen

Französisch
Muttersprachlich oder zweisprachig
Englisch
Verhandlungssicher
Deutsch
Konversationssicher

Arbeitsortpräferenzen

Vor Ort möglich

Bâle (bis zu 30 km), Mulhouse (bis zu 10 km), Colmar (bis zu 10 km), Strasbourg (bis zu 10 km), Fribourg-en-Brisgau (bis zu 10 km)

DataForGood
Developer for EuroforDocs
VEREINE & GEWERKSCHAFTEN
Januar 2026 - Heute (5 Monate)
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
UMWELT
Februar 2026 - März 2026 (1 Monat)
Huningue, Frankreich
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
GESUNDHEIT & WELLNESS
November 2025 - Dezember 2025 (1 Monat)
Huningue, Frankreich
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