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Arthur DANJOU ba91408b6d feat: Add personal profile, projects, and skills documentation

- 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.

2025-12-22 19:39:36 +01:00

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title	description
Arthur Danjou - Data Science & Applied AI Student.	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.

4.6 KiB Raw Blame History