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Mohamed Aymen BouyahiaMA

Mohamed Aymen Bouyahia

Data Scientist

€300/day
Paris, FR
3-7 years
Deep Learning
Artificial Intelligence
Data Analysis
Data Visualization
Data Scientist LLM

Average response time: 1 hour

Freelancer profile translated to English.
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About Mohamed Aymen

Hello,
My name is Aymen. I work as a Data Scientist at LCL bank.
I have worked on RAG projects, AI agents, Information Extraction. Currently, I specialize in securing LLMs to deploy customer-facing agents.
Banking and Financial Services

  • Arabic

    Native or bilingual

  • French

    Native or bilingual

  • English

    Fluent

  • Spanish

    Basic

Can work on-site
Paris (up to 50km)

Experience

  • LCL
    Data Scientist
    February 2025 - Today (1 year and 6 months)
    Île-de-France, France
    Permanent Job in the AI Team of the bank LCL (Credit Lyonnais)
    – Multilabel classification of banking documents using the Donut model, trained on distributed GPUs with Accelerate library.
    – Use of the models gemini-2.5-pro, mistral-small and claude-3.5-sonnet-v2 for information extraction.
    – Working on GUI Agent using Computer-use repository made by Google Deepmind.
    – Development of an Agentic RAG to provide real-time, accurate answers for bank advisors' queries.
  • Crédit Agricole Assurances
    Data Scientist Intern
    April 2024 - October 2024 (6 months)
    Île-de-France, France
    Detection of document fraud in medical insurance claims submitted by clients.
    – Development of an OCR-based information extraction system using a voting mechanism integrating Tesseract, Doctr, and Donut.
    – Development of a ResNet-based autoencoder specifically designed to detect signs of forgery in documents.
    – Deployment of an angle deskewing tool (jDeskew) based on Fourier Transform.
    – Deployment of a perspective deskewing tool based on Segment Anything Model and openCV.
  • Ens Paris Saclay
    Machine Learning Researcher Intern
    April 2023 - August 2023 (4 months)
    Île-de-France, France
    Development of an improved version of LIME, used in explainability, using Neural Decision Trees.
    – Development of an interpretable deep learning model named Neural Decision Tree.
    – Elaboration of metrics to evaluate explanations: Fidelity & Stability.
    – Creation of library called LIME_NDT that uses Neural Decision Tree for explanation instead of Linear Regression.
    Data Analysis Deep Learning Artificial Intelligence

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Education

  • dual degree in Data Science
    ENSTA Paris
    dual degree in Data Science
  • Master 2 Data & AI
    Polytechnic Institute of Paris - IPP
    2024
    Master 2 Data & AI
Check out Mohamed Aymen's education

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