Required skills
- Data Science
- Azure Databricks
- MLOps
- SQL
- Azure DevOps
- MLflow
- Git
- Predictive Modeling
- Prescriptive Modeling
- Data Analysis
- Data Preparation
- Model Deployment
Languages: French
Job description
Our client is looking for a Senior Data Scientist specialized in Azure Databricks and MLOps to work on technically and operationally demanding Data Science projects. The goal is to leverage data for value-added analysis, develop predictive and prescriptive models, and ensure their industrialization and production monitoring.
Project overview
Our client is looking for a Senior Data Scientist specialized in Azure Databricks and MLOps to work on technically and operationally demanding Data Science projects.
The challenge: leverage data to produce value-added analyses, develop predictive and prescriptive models, and ensure their industrialization and production monitoring.
You will work in a technological environment relying notably on Azure Databricks, MLflow, SQL, and Azure DevOps, with real responsibility for the entire model lifecycle.
Job Description
Your mission: transform data into robust, industrialized, and production-ready predictive solutions.
As a Senior Data Scientist Azure Databricks / MLOps, you will take charge of Data Science projects end-to-end, from data exploration to production deployment and model monitoring.
Responsibilities
Job Challenges
Beyond modeling, this mission requires a real ability to move from experimentation to reliable production solutions. You will need to combine technical expertise, functional understanding, and autonomy to propose relevant and sustainable solutions.
- Analyze and prepare data: explore datasets, identify relevant insights, and perform the necessary processing for their exploitation.
- Design Data Science models: develop classification, segmentation, and predictive and prescriptive analysis models.
- Manage the model lifecycle: ensure their design, training, evaluation, and production deployment on Azure Databricks.
- Industrialize models with an MLOps approach: implement best practices for deployment, monitoring, and maintenance of models in production, particularly through MLflow, Git, and Azure DevOps.
- Ensure solution performance: define KPIs, set up monitoring and alerting mechanisms, and track performance over time.
- Ensure the quality and sustainability of developments: perform unit and non-regression tests, write technical and functional documentation, and handle evolutionary and corrective maintenance.
Project details
- Start: flexible
- Remote: hybrid
- Location: Paris, France, FR
- Languages: French
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