Alexandre Martin

Experienced Lead Data Scientist / Machine Learning Engineer with a background in finance and statistics, complemented by a Master's in Machine Learning from Télécom ParisTech. I have always had a strong interest in deploying predictive models into production.

I have had the opportunity to work on complex and varied use cases requiring strong technical skills in machine learning/deep learning, a deep understanding of best practices in coding and deployment, and a thorough comprehension of business needs.

Most recently, I worked on the design, development, and deployment of marketing performance attribution models (Marketing Mix Model) at Merck and, more recently, at Pernod Ricard, where I am currently a Lead Machine Learning Engineer.

These experiences have allowed me to be involved in all stages of predictive model deployment within an organization, from data collection to production, including the Proof of Concept (POC) phase.

My goal is to leverage this expertise to support you in your project. Pragmatic, determined, and a natural communicator, I enjoy discussing my work with other teams, always keeping the final result and maximizing your ROI in mind.

I wish to provide you with assistance in decision-making on a technical, business, and managerial level, by being proactive in the various stages of your project.

Technical Stack:

Programming: Python, Spark

Machine learning / deep learning: Scikit-learn, XGboost, Pytorch, TensorFlow

Vision: OpenCV, Pillow

LLMs: Openai GPT, Langchain.

Open-source framework: Hugging Face, Mistral, LLama 2

Cloud: AWS (SageMaker, Lambda, EMR, API Gateway, S3, CloudFormation, DynamoDB), Azure: Azure ML, Azure DevOps (CI/CD).

ML deployment: Mlflow

DevOps: Git, Docker, Kubernetes

API: Fast API, Flask

Solution: Dataiku