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Soufiane AaziziSA

Soufiane Aazizi

Senior Data scientist, Ph.D

€950/day
2 projects
Paris, FR
8-15 years

Average response time: 1 hour

About Soufiane

Lead AI Engineer with 12+ years of experience in data science, quantitative finance, and machine learning — now specialized
in Generative AI and LLM systems. Designed and deployed agentic AI architectures, RAG pipelines, and multi-agent
frameworks in regulated industries (pharma, banking, transport). Background in quantitative strategies (Société Générale,
Thomson-Reuters, RBC Dexia). Fluent in English, French, and Arabic. Based in France, open to international opportunities.
AWS ML Certified.
  • English

    Fluent

  • French

    Native or bilingual

  • Arabic

    Native or bilingual

Can work on-site
Paris (up to 50km), Paris (up to 100km), Lille (up to km), Toulouse (up to 100km)

Experience

  • Servier Laboratory
    Lead Gen AI
    PHARMACEUTICALS INDUSTRY
    December 2024 - Today (1 year and 6 months)
    Suresnes, France
    • Developed Agentic ICF system that transforms complex Clinical Study Protocol tables (spanning multiple pages, dozens of columns and rows) into concise ICF Summary Tables — reducing generation time from 1–2 days (manual) to under 5 minutes, leveraging Skills Cards architecture with scoped MCP tool restrictions.
    • Developed Vision Agentic RAG system with DSPy serving a global team of medical writers; improved retrieval hit-rate@6 from 60% to 85% across thousands of indexed documents and images.
    • Built custom document parser using Docling to extract tables, figures, and images from complex PDFs, RTF, and DOCX into structured Markdown and metadata; indexed into Weaviate (text) and Google Cloud Storage (thousands of images).
    • •Designed comprehensive evaluation framework assessing parsing quality, retriever performance, anti-hallucination robustness, and answer generation effectiveness.
    • •Re-engineered monolithic application with full streaming architecture, reducing long-running task response time from 2 minutes to ~4 seconds.
    Technologies: DSPy, Agentic-RAG, Compound AI, Docling, Weaviate, Vertex AI, GCS/GCP
    DSPy Weaviate Docling Google cloud RAG
  • KPMG
    Lead Data Scientist
    CONSULTING AND AUDITS
    April 2024 - November 2024 (7 months)
    Paris, France
    • Led a team of 5 Data Scientists; delivered POC in 2 months and production-ready RAG chatbot with full UI in 3 months, parsing thousands of documents (PDF, PPTX, images) for the audit department.
    • •Designed compound AI architecture with DSPy optimizers: query decomposition, chain-of-thought reasoning, and multi-hop document traversal — improving answer accuracy from 70% to 94%.
    • Implemented Azure Search reranking and enhanced recursive retrieval with DSPy for dynamic keyword generation; achieved ~4-second streaming response time.
    • Integrated LangFuse for real-time monitoring, performance evaluation, and feedback collection.
    Technologies: DSPy, Azure Search, LangFuse, Structure.io, Pytesseract, GPT
    DSPy Cloud Azure LangFuse GPT4 azure searrch
  • SNCF-Connect
    Lead Data Scientist
    TRANSPORTATION
    November 2023 - March 2024 (4 months)
    Paris, France
    • Designed and implemented QA ChatBot leveraging LlamaIndex RAG and LangChain, covering dozens of FAQ topics with sub-50ms retrieval latency, eliminating manual searches for support agents.
    • Employed auto-retriever composition on Vespa.ai for enhanced passage retrieval; implemented LangFuse monitoring for performance evaluation and continuous FAQ enrichment.
    Technologies: LangChain, LlamaIndex, DSPy, Amazon Bedrock, Vespa.ai, OpenAI, LangFuse
    Langchain vespa.ai LlamaIndex

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Education

  • Docteur en Mathématiques Appliquées
    Université Cadi Ayyad
    2016
    • Approximation discrètes des Equations différentielles Stochastiques Rétrogrades • Contributions à l'étude des processus de Lévy et des processus fractionnaires via le calcul de Malliavin et applications en statistiques • Le théorème central limite en probabilité et statistiques pour les mouvements Browniens sous-fractionnaires et bi-fractionnaires • Problème de portefeuille avec contraints stochastiques • Problème de switching avec contrainte
  • Master recherche (MASEF): Mathématiques Appliquées à la Finance à l’Economie & l’Assurance -
    Université Paris DAUPHINE – ENSAE
    2008
    Master recherche (MASEF): Mathématiques Appliquées à la Finance à l’Economie & l’Assurance -

Certifications

Skill set (38)

Categories