sskl
I'm a data scientist and ML engineer with extensive experience building end-to-end AI systems across healthcare, finance, and tech.
About
I’m a data scientist and ML engineer with extensive experience building end-to-end AI systems across healthcare, finance, and tech. I design robust data pipelines, ML models, and production-ready backends, with a passion for turning data into actionable decisions. I thrive in cross-functional environments and enjoy mentoring teammates to deliver scalable, impactful solutions.
I am motivated by solving complex problems with modern AI tooling, driving production deployment and MLOps practices, and continuously learning new techniques to improve model performance and reliability.
- 10 years' experience
- Joined November 2023
Skills· 21
Languages
- FrenchFluent
- EnglishFluent
- ArabicFluent
Industry experience
- Software & Internet
- Financial Services
- Healthcare
- Professional Services
- Life Sciences
- Media & Entertainment
Work experience· 6 roles
Created a Hybrid RAG research system for researchers and commercial potential matching. Built end-to-end data extraction pipelines for medical biology classification for research publications, startups, and pharmaceutical company research interest. Built high-efficiency document crawlers for scientific publications. Key technologies: Python, AWS, Modal, OpenAI API, OpenAlex, Vector Store, NLP, LangChain, Postgres.
Lead ML design and deployment across organizational projects to optimize production costs per country; lead ML for projects moving to production (architecture, data access, monitoring, feature engineering). Active member of the MLOps task force; benchmarking LLMs for internal use; tooling and governance.
Designing and building Gen AI-based lead generation and matching platform; backend with ML models; ML/DL pipelines; cross-cloud interoperability; mentoring team; deployment.
Education
The master Machine Learning for Information and Content is designed to train a good data-scientist. This unique program combines essential skills training in machine learning, content analysis and distributed acces to create knowledge from bigdata. The questions we address are: -The technical skills to become a good data-scientist -Models and methods to learn from real and big data: text, speech, image, the web, social network, and from scientific or industrial applications -The design of scalable algorithms that can learn and infer knowledge and prediction
Certifications and awards· 5
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