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Mohammed Azizi

Mohammed Azizi

Lead Data & Machine Learning Engineer

Courbevoie (92400) France
Employed Open to opportunities
With a strong expertise in artificial intelligence and data analytics, I have had the opportunity to work on and lead end-to-end projects in various fields such as finance and healthcare, delivering value to each profession through specific use cases that seamlessly integrate into both business and technical workflows.
  • Lead and Develop AI and Data Analytics Initiatives for Natixis' Asset & Wealth Management Division. Key Projects:
    • Generative AI : Design and implement generative AI solutions within a highly secure environment, using cutting-edge technologies such as quantized Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. The goal of the use case is to provide the business team with a GenAI tool that allows them to interact with internal databases using natural language.
    • Fund Performance Analytics Platform: Design a data analytics platform to monitor fund performance by integrating external and internal data. This includes a dashboard for in-house versus competitor fund comparisons and an email alerting system for key performance indicators.
    • Client 360: Develop data analytics projects that involve extracting, loading, and analyzing customer engagement data to construct comprehensive 360° dashboards. Our goal is to leverage data insights to support strategic decision-making and provide a 360 view of client engagement channels.
  • Technical Environments & Tools: Azure Data Factory, Azure Databricks, Azure Machine Learning, GCP, Vertex AI, Azure OpenAI, Informatica, Terraform, Jenkins, Tableau, BigQuery, Spark, XLDeploy, PowerBI, Airflow
  • Co-founder and head of the AI department at the startup DataPathology, specialized in digitizing the processes of pathological anatomy and using AI approaches for cancer detection. I was responsible for leading and developing AI solutions, particularly for breast cancer.
  • My role includes the following:
    • Implementation of the scientific approach to address the cancer diagnosis problem
    • Implementation of the technical approach
    • Technical and clinical evaluation of the approach
    • Development and deployment of a web application to make the application available to doctors
  • Technical Environments & Tools: AWS, Flask, IIS, Azure DevOps
  • Build Machine Leaning solutions for the CIB branch of Natixis. Key Projects:
    • Interest Rate Hedging: Building a tool that automatically helps manage the risks of interest rate changes for derivatives making smarter decisions by using recurrent neural networks to optimize the hedging.
    • Use NLP to analyze the minutes from client minutes, and turning their words into actionable insights
    • Smart Recommendations for Hard-to-Sell Assets.
  • Technical Environments & Tools : Azure Machine Learning, MLFlow, Spark, Azure DevOps, XLDeploy, PowerBI, Hadoop, Docker
  • Build a cognitive platform for Information Retrieval tasks that improves our understanding of unstructured and semi-stuctured document, primarily using NLP & Computer Vision techniques. Key features of the platform include:
    • Named Entity Recognition to accurately extract specific entities from PDF documents
    • Entity Relationship Extraction
    • Deep Learning system for efficient table extraction and comprehensive document layout analysis.
    • Create a web API for the beta testing phase of the tool, facilitating user interaction and feedback.
  • Technical Environment & Tools : Python, Tensorflow, Flask, Spacy