As a Senior Data Scientist , you will be responsible for the following tasks:

- Development of predictive and prescriptive models:

• Design and train Customer Segmentation models (clustering, RFM, behavioral profiling).
• Develop personalized recommendation engines (collaborative filters, content-based, hybrid systems) to improve cross-selling and upselling.
• Developing Prediction and Detection Models: Churn, propensity, risk scoring, transaction volume forecasting and detection of anomalies / behavioral rules for the fight against money laundering (AML / Anti Money Laundering)

- Data and model engineering:

• Collaborating with Data Engineers to define features and prepare the necessary training pipelines from the Datalakehouse (Cassandra, Hive, etc.).
• Implement MLOps best practices : model versioning, deployment automation, production performance monitoring (drift, bias).

- Added value to the business:

• Translate business needs (e.g., reduce churn, increase conversion rate) into Data Science problems.
• Interpret the model results and communicate the insights to Product Owners and business stakeholders.
• Contribute to enriching source systems and the data lakehouse with predictive data. Company description

Education: Engineering degree (Bac+5) with a specialization in Data Science, Artificial Intelligence or Statistics/Applied Mathematics.

Desired Candidate Profile

• Education: Engineering degree (Bac+5) with a specialization in Data Science, Artificial Intelligence or Statistics/Applied Mathematics.
• Experience: 35 years of confirmed experience on similar predictive modeling and recommendation systems projects.
• Comp tences Techniques :
• Mastery of ML: Classical algorithms (regression, classification, clustering, time series) and Deep Learning (if applicable).
• Frameworks IA : Ma trise des biblioth ques Python (Scikit-learn, Pandas, XGBoost, TensorFlow ou PyTorch).
• Big Data & Tools: Knowledge of distributed environments (Spark/PySpark) and NoSQL databases (Cassandra, MongoDB).
• Ops & CI/CD: Familiarity with containerization environments (Docker) and model lifecycle management (MLOps).
• technological environment: Cloudera Data Platform, Microservices Architecture, Kafka, API Gateway (Axway), Keycloak.
• Methodology: Working in Agile mode (Scrum of Scrums, weekly follow-up committees).
• Collaboration : quipe pluridisciplinaire (Data Engineers, Lead Tech, Product Owners, Experts m tier).