Freelancer profile translated to English.
Back to original languageAbout Landry
🎯 Junior Data Scientist, graduated from a program focused on applied projects (OpenClassrooms x CentraleSupélec), with a quantitative background and actuarial experience.
I am currently looking to consolidate my data science skills through a professional immersion mission, equivalent to an internship, focusing on concrete data challenges.
During my training, I worked on projects covering the main stages of a data project: exploratory analysis, machine learning modeling, model deployment, and results visualization, particularly in cloud environments (AWS).
These projects allowed me to acquire solid technical foundations and an initial approach to industrializing data solutions.
I am particularly interested in missions that allow me to develop my skills by working with experienced data teams, especially on topics related to modeling, data processing, pipelines, or production deployment.
📊 Key Skills (Junior)
• Data analysis and preparation (ETL, EDA)
• ML Modeling (scoring, NLP, computer vision – project level)
• Deployment and visualization (Streamlit, dashboards)
• Cloud environments (AWS – operational knowledge)
🛠️ Stack: Python, R, SAS, Prophet, Spark, Docker, Git/GitHub Actions, Dataiku (basic knowledge), AWS, Streamlit, Plotly..
📍 Available immediately – on-site or remote (All of France, but priority to Île-de-France)
French
Native or bilingual
English
Fluent
Can work on-site
Thiais (up to 50km), Lyon (up to 50km), Bordeaux (up to 50km), Lille (up to 50km), Rennes (up to 50km)
Experience
- OPENCLASSROOMSData Scientist: Implementation and Deployment of a Scoring ModelCIVIC AND SOCIAL ORGANIZATIONSNovember 2024 - February 2025 (3 months)Paris, FranceFull implementation of a supervised machine learning project, from conception to production deployment in a cloud environment:Building an automated training pipeline including data preparation, algorithm testing, and model serialization with MLflow for reproducible tracking.Supervised modeling strategy: comparative evaluation of several algorithms, hyperparameter optimization, and class imbalance management.Rigorous performance evaluation using distinct datasets (train/test) and metrics adapted for binary classification (ROC AUC, F1-score, precision…).Monitoring data drift to anticipate performance degradation in production.Deployment of the model as a REST API with Flask, hosted on a cloud platform (Heroku), enabling continuous integration.Use of Git and GitHub for code versioning and collaboration.
- OPENCLASSROOMSData Scientist: Automatic Classification of Consumer GoodsCIVIC AND SOCIAL ORGANIZATIONSJuly 2024 - October 2024 (3 months)**Automatic Classification of Consumer Goods**:
- Data: Images, description, and product category
- Domains: NLP, computer vision, supervised and unsupervised learning.
1. Text data preprocessing to obtain usable dataset2. Graphical representation of high-dimensional data3. Image data preprocessing to obtain usable dataset4. Implementation of dimensionality reduction techniques5. Use of data augmentation techniques6. Definition of data collection strategy by identifying available APIs7. Definition of deep learning model development strategy8. Evaluation of deep learning model performance according to different criteria - Abeille AssurancesActuary Capital Management - ApprenticeshipBANKING AND INSURANCESeptember 2023 - March 2025 (1 year and 6 months)Bois-Colombes, FranceWithin the Capital Management Actuarial team, I contributed to the modeling and analysis of new business profitability, as well as ALM (Asset Liability Management) studies, in the context of financial management of a life insurance portfolio.Main tasks:Design and implementation of projection tools for calculating profitability indicators (NBV, NBM) and value creation (VIF, Surplus).Participation in ALM studies, with an analysis of the impact of asset allocation strategies on economic (real world) and prudential (own funds, SCR, solvency ratio) metrics.Use of Prophet for cash flow projection and actuarial modeling integrated into the ALM tool.Python development for the allocation of projected indicators (NBV, VIF…) by model point.Automation using Excel/VBA for extracting, reprocessing, and analyzing results from Prophet.
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Education
- Professional Certification "Data Science Expert" Level 7 (Master's degree in Data Science)OpenClassrooms x CentraleSupélec2025Formation orientée projet avec 10 projets dont 9 techniques avec des données réelles et des missions de types entreprise. Outils utilisés : Jupyter Notebook ; Git/GitHub; Cloud computing (Infrastructure as a Service : AWS EC2 ; Platform as a Service: Heroku); SQL Lite [Quelques projets : -Analyse de données du système éducatif : Exploration et visualisation de données -Anticipez les besoins de consommation des bâtiments (gaz et électricité) : modélisation prédictive et interprétation des modèles avec SHAP. -Classification automatique de biens de consommation : NLP , vision par ordinateur, apprentissage supervisé et non supervisé. -Implémentation et déploiement d’un modèle de scoring : déploiement d’API Backend et utilisation de services cloud (Heroku); développement d'un frontend avec Streamlit]
- Master's in Actuarial Science - FinanceUniversity of Montpellier2022Actuariat : Machine Learning pour l'assurance /Tarification vie et non vie /Provisionnement technique /Solvabilité 2 / Gestion Actif-Passif et modélisation ALM/ Traités de réassurance/ Finance : Calcul stochastique /Pricing d'instruments financiers /Gestion de portefeuille Logiciels utilisés : Python, R, Excel/VBA, E-Views