Freelancer profile translated to English.

Description

Mechanical engineer by training, I then pursued an AI developer training at Simplon. With my strong interest in mathematics and the Python language, I chose to specialize in data. After my apprenticeship at AG2R as a Data Scientist, I carried out a database cleaning mission during which I was able to improve my skills in Excel and VBA.

My AI developer training allowed me to learn several skills that I can put to use for your company, such as:

- Designing Django / Python web applications

- Designing web applications with Streamlit

- Developing APIs (FastAPI)

- Creating dashboards (PowerBI) -> see AI Dev training

- Querying databases (SQL)

- Using the GCP platform (especially BigQuery) -> see AG2R experience

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Lille (up to 50km), Paris (up to 50km)

Emerson
Data Cleansing Engineer
October 2024 - December 2024 (3 months)
Armentières, France
As part of the migration of data from SAP to Oracle:
- Extraction of SAP data
- Categorization of different data types (plans, notes, etc.)
- Standardization and cleaning of data
Work on Excel (VBA)
AG2R La Mondiale
Data Scientist
October 2023 - October 2024 (1 year)
Lille, France
In the Data IA Factory team, whose objective is to develop tools integrating AI to facilitate daily tasks.
Examples of projects:
- Development of a web application to record, transcribe, and categorize customer calls
- Development of a web application that transcribes meeting audio and automatically generates a summary

I worked on several projects using Google Cloud Platform, particularly BigQuery for data storage and analysis, as well as Vertex AI for using Machine Learning models.
I used GCP services from the console as well as with APIs and Python.

🔹 Development and deployment of Streamlit applications in Python on GCP to visualize the results of AI processing.
🔹 Use of Cloud Storage to store and organize audio files for model input.
🔹 Testing, validation, and adjustment of AI models used for audio transcription and summarization with Vertex AI.
🔹 Data processing and analysis with BigQuery, including:
- Creation and management of tables to store transcriptions, summaries, and durations of audio files.
- Design of table schemas optimized for storing data from AI processing (transcriptions, summaries, audio durations, file links).
- Execution of advanced SQL queries to clean and structure data (removal of special characters, filtering of irrelevant words, duplicate detection, statistical calculations on audio durations).
- Automation of data loading from Cloud Storage using BigQuery APIs in Python.
🔹 Log management on GCP (Cloud Logging) to track processing execution, resolve errors, and optimize the performance of BigQuery queries and Vertex AI models.
🔹 Use of Firestore to store metadata related to audio files and processing, in addition to BigQuery
BigQuery Google Cloud Platform (GCP) Vertex AI Streamlit Python Development
TERAO
Bioclimatic Modeling and Design Engineer
June 2021 - September 2021 (3 months)
Paris, France
Modeling the thermal behavior of buildings (Design Builder)
Writing BREEAM assessment reports

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AI Data Developer
Ecole Microsoft by Simplon
2024
Projet Power BI : 1. Connexion et importation des données - Connexion à une instance SQL Server déployée sur Azure. - Sélection de la base de données AdventureWorks2019DW (bdd exemple de Windows). - Choix et importation des tables nécessaires pour l’analyse. 2. Préparation et transformation des données - Utilisation de Power Query pour nettoyer et transformer les données. - Application de filtres et transformations pour structurer les données. - Ajout de colonnes calculées pour enrichir l’analyse. - Création de mesures DAX pour calculer des KPIs (ventes totales, moyennes, taux de croissance, etc.). 3. Création du tableau de bord interactif - Conception d’un tableau de bord Power BI avec 2 pages distinctes. Sales Overview : Vue d’ensemble des ventes, tendances et performances commerciales. Customer Insights : Analyse des clients (préférences d’achat, segmentation par âge et pays). 4. Visualisation des données et interactivité - Utilisation de graphiques variés (barres, courbes, cartes, camemberts, etc.). - Ajout de filtres et slicers pour rendre le tableau de bord interactif. 5. Finalisation et optimisation - Vérification de la cohérence des données et des indicateurs. - Optimisation des performances du rapport (réduction des requêtes inutiles).
Data Professions Pre-qualification SAS
SIMPLON
2024
SAS de préqualification aux métiers de la data