mirror of
https://github.com/ArthurDanjou/artsite.git
synced 2026-01-14 18:59:59 +01:00
Arthur DANJOU
ba91408b6d
- Created index.md for personal introduction and interests. - Added languages.json to specify language proficiencies. - Developed profile.md detailing academic background, skills, and career goals. - Introduced multiple project markdown files showcasing personal and academic projects, including ArtChat, ArtHome, and various data science initiatives. - Implemented skills.json to outline technical skills and competencies. - Compiled uses.md to document hardware and software tools utilized for development and personal projects.
98 lines
4.6 KiB
Markdown
98 lines
4.6 KiB
Markdown
---
|
|
title: Arthur Danjou - Data Science & Applied AI Student.
|
|
description: Profile of Arthur Danjou, a Data Science and Applied AI student at Paris Dauphine-PSL University, highlighting his skills, experience, and career aspirations.
|
|
---
|
|
|
|
# Arthur Danjou
|
|
**Data Science & Applied AI**
|
|
|
|
Rigorous, curious, and motivated, I put my skills in statistics, machine learning, and applied artificial intelligence to work on concrete and innovative projects[cite: 9]. Passionate about mathematical modelling and the deployment of AI solutions, I enjoy transforming theory into measurable results[cite: 10].
|
|
|
|
## 📞 Contact & Links
|
|
|
|
* **Email:** `arthur.danjou@dauphine.eu`
|
|
* **Portfolio:** `go.arthurdanjou.fr/portfolio`
|
|
* **GitHub:** `go.arthurdanjou.fr/github`
|
|
* **LinkedIn:** `go.arthurdanjou.fr/linkedin`
|
|
|
|
## 📍 Location
|
|
|
|
* **Current:** Paris, France
|
|
* **Timezone:** Europe/Paris (CET/CEST)
|
|
* **Remote:** Open (confirmed by "REMOTE" experience)
|
|
|
|
## 🗓️ Availability
|
|
|
|
* **Status:** Available for a final-year internship.
|
|
* **Start Date:** **April 2026**.
|
|
* **Contract Types:** Internship (priority).
|
|
|
|
## 🎯 Career Goals
|
|
|
|
* To join a team of Data Scientists or AI Researchers to deepen my knowledge.
|
|
* Contribute to high-impact projects.
|
|
* **Prepare for a future doctorate in artificial intelligence**.
|
|
* Become an expert in Machine Learning Engineering and MLOps.
|
|
* Combine mathematical rigor (from education) with practical engineering solutions (from experience).
|
|
|
|
## 💼 Work Preferences
|
|
|
|
* **Target Roles:** Data Scientist, AI Researcher.
|
|
* **Industries:** AI/ML, Data Science, Health, DevOps.
|
|
* **Work Style:** Remote, Hybrid.
|
|
* **Company Size:** Startup, Scale-up, Enterprise.
|
|
|
|
## 🏆 Notable Achievements
|
|
|
|
* Administration of a personal home lab (servers, databases, storage, backups) for MLOps experimentation since 2022.
|
|
* Implemented daily data cleaning pipelines on Azure with PySpark, enhancing data quality by 20-30% at Sevetys.
|
|
* Design, development, and maintenance of web, data, and cloud projects (Python, TypeScript, Nuxt 3) via ArtDanj Production.
|
|
* Developed an automated monthly data quality report (completeness, consistency) for business teams.
|
|
|
|
## 📚 Education
|
|
|
|
* **Master's in Applied Mathematics** (M280) - Paris Dauphine-PSL University (2023-2025)
|
|
- Specialization: Data Science & Applied Artificial Intelligence
|
|
- Focus: Deep Learning, Probabilistic Models, Statistical Learning Theory
|
|
* **Bachelor's in Applied Mathematics** - Paris Dauphine-PSL University (2020-2023)
|
|
- Foundation in linear algebra, probability, statistics, and numerical analysis
|
|
|
|
## 🔐 Certifications & Competencies
|
|
|
|
* Advanced Python & Data Science practices
|
|
* MLOps & Cloud Infrastructure (Azure, Docker, Kubernetes)
|
|
* Full-stack Web Development (Nuxt 3, TypeScript, Vue.js)
|
|
* Linux System Administration & Networking
|
|
|
|
## 🎓 Research & Academic Interests
|
|
|
|
* Machine Learning Engineering and deployment pipelines
|
|
* Probabilistic inference and Bayesian methods
|
|
* Statistical learning theory and generalization bounds
|
|
* Deep Learning architectures for structured data
|
|
* Data quality and governance in production systems
|
|
* Former rugby team captain, participating in the French school championships.
|
|
|
|
## 📚 Education & Core Competencies
|
|
|
|
### Paris Dauphine-PSL University (MSc)
|
|
|
|
* **Dual Expertise:** Theory & Practice in Advanced Data Science and AI Systems.
|
|
* **Core Theoretical Focus:** Strong background in statistical modeling and advanced AI principles (Advanced Machine Learning, Learning Theory, Clustering, Deep Learning, Climate Risk Modeling).
|
|
* **Practical Skills:** Hands-on experience in NLP, Reinforcement Learning, Generative AI, Data Quality, and Data Visualisation.
|
|
* **Key Courses (M1/M2):** Supervised Statistical Learning, Generalised Linear Models (GLMs), Monte Carlo Methods, Data Analysis, Non-parametric Statistics, Time Series, Numerical Optimisation.
|
|
* **Active Engagement:** Scheduled participation in the Natixis and DIRISI Hackathons.
|
|
|
|
### Technical Skillset
|
|
|
|
* **Programming:** Python, R, TypeScript, Java, Git, LaTeX.
|
|
* **Libraries & Frameworks:** NumPy, Pandas, Scikit-learn, PyTorch, Matplotlib, Seaborn.
|
|
* **Databases:** SQL, Redis.
|
|
* **Cloud & DevOps:** Proxmox, Docker, Azure, Linux, SysAdmin.
|
|
|
|
### Statistical & AI Modeling
|
|
|
|
* **Multivariate Data Analysis:** Principal Component Analysis (PCA), Correspondence Analysis (CA), clustering techniques, correlation analysis.
|
|
* **Supervised Learning:** k-NN, linear and logistic regression, CNN, Naive Bayes.
|
|
* **Unsupervised Learning:** Clustering, dimensionality reduction, k-means, CNN.
|
|
* **IA & Modern Models:** Natural Language Processing, Transformers, Large Language Models, AI agents, embeddings, and fine-tuning. |