artmcp/content/profile.md

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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.
• 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.