---
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.