Freelancer profile translated to English.

Description

PhD in Computer Science, expert in Data Science & AI with over 10 years of experience in designing and deploying artificial intelligence solutions in industrial and medical environments. Specialist in Machine Learning, Deep Learning, cloud, and MLOps, I ensure the success of complex projects end-to-end, from scoping and architecture to industrialization, while supervising multidisciplinary technical teams.

Languages

French
Native or bilingual
English
Fluent
Arabic
Native or bilingual

Workplace preferences

Can work on-site

Lyon (up to 50km)

PrediSurge
PhD. Lead Data Scientist
MEDICAL
January 2024 - Today (2 years and 5 months)
PhD – Lead Data Scientist
I lead the scientific and technical dimension of AI and MLOps projects,

Technical & Strategic Leadership: definition of scientific directions, supervision of R&D, and management of AI projects.

Predictive Models: design and deployment of predictive models for EVAR procedures.

Medical Imaging: development and optimization of neural networks for automatic segmentation of vascular structures (pre- and post-operative).

MLOps & Cloud: implementation of a complete AWS infrastructure and a CI/CD pipeline to manage the entire model lifecycle (training, validation, deployment).

Interdisciplinary Collaboration: working with medical, scientific, and technical teams to transform business needs into concrete solutions.

Industrialization & Best Practices: definition of standards (Model Cards, data & model versioning, GitFlow, tests) and support for teams in their adoption.
artificial intelligence Amazon Web Services Research and development MLOps Data science
Hospices Civils de Lyon
PhD. Senior Data Scientist
MEDICAL
September 2020 - January 2024 (3 years and 4 months)
PhD – Senior Data Scientist
Member of the AI commission of the Hospices Civils de Lyon, I support strategic projects in public health and biomedical research, at the intersection of artificial intelligence, statistics, and biology. My goal is to transform complex clinical needs into concrete solutions using advanced AI models.

I design and deploy predictive models and analysis tools, while implementing MLOps infrastructures (MLflow, Git/GitLab, Docker, AWS) and industrializing data pipelines. Accustomed to interdisciplinary environments, I collaborate with doctors, researchers, and engineers, while supervising teams (3 to 5 people) and disseminating best practices for development and scientific rigor.

Some notable achievements:

NLP tool based on BERT for analyzing patient feedback (HCL Innovation Laureate), with a complete MLOps stack.
Assisted diagnosis of oral cancer via computer vision (CNN, Fusion Learning) in collaboration with McGill University.
Prediction of heart diseases within the ANR PEPR Santé Numérique project (multi-scale models and hybrid digital twins).
Big data analyses from the Digital Spatial Profiler (clustering and supervised learning on multimodal data).
Contribution to the European QUALITOP project, an AI platform for personalized monitoring of patients under immunotherapy.

Key Skills: Machine Learning, Deep Learning, NLP (BERT, multi-label classification), Computer Vision (CNN, fusion learning), MLOps (MLflow, AWS, Git/GitLab, Docker), Python, R, advanced statistics, multimodal data analysis, agility.
artificial intelligence Data science Cloud computing MLOps Python
OpenClassrooms
Mentor in Data Science & Data Analyst
June 2021 - December 2023 (2 years and 6 months)
Mentoring Data Science, Machine Learning, and AI students through concrete projects based on real-world problems. I provide technical and methodological mentoring from introductory to industrialization levels (MLOps and cloud), with a simple goal: to make learners autonomous, capable of delivering robust, evaluated, and deployed solutions.

Academic Mentoring

Clarification and deepening of fundamental and advanced topics: Big Data, ML, Deep Learning, AI, statistics and probability, MLOps.

Implementation of Best Practices: needs scoping, metric selection, traceability and versioning, cross-validation, reproducibility, CI/CD, and deployment.

Supervised Projects

ML in the cloud (AWS), scoring models, market studies, and forecasts.
Predictions in the energy sector, automatic classification, deep learning-based "bad buzz" detection.
Relational database design, data pipelines, model serving APIs (FastAPI, Flask).

Technologies & Skills

Languages: Python, R. Big Data & data: PySpark, SQL, NumPy, pandas.
ML/DL: scikit-learn, TensorFlow; classification, regression, clustering tasks; optimization and tuning (grid/random/Bayes).
Analysis & visualization: PCA, t-SNE, matplotlib; storytelling and communication of results.
MLOps & cloud: MLflow, Git/GitLab, Docker, AWS; packaging, experiment tracking, monitoring.
Software engineering: tests, code reviews, documentation.

Mentoring Formats

Courses, practical workshops, and project sprints.
Notebook and architecture reviews, quick code audits, "pair programming" coaching.
Evaluations and actionable feedback, individualized skill development plan.

Result: realistic projects, measured models (ROC, AUC, precision/recall), and controlled deployments, preparing for real-world production.
Python Cloud computing MLOps Amazon Web Services Machine learning