Médérick Samba

• Support Product Owner Lead on CDP Loyalty Program Integrations Sync different teams involved in the Loyalty Program to make it happen. Apply Business rules for Golden Record creation. • Lead Data integrator for CDP. Leading technical implementations to match business needs. Empowering new technical members for their right contribution. Putting in place Python Notebook in and outside of CDP to meet business needs. • Help data transition from CDP to new architecture Using Databricks, Azure Datalake storage Defining Bronze / Silver / Gold (Medallion) architecture with team members Providing Business datasets for visualization need

Data Analyst – Various Banking and Marketing Projects • Developed SQL scripts for data auditing across multiple banking institutions Recalculated interest amounts Detected anomalies and inconsistencies in financial data Built dashboards using Tableau for data visualization Presented insights and results to Partners and senior stakeholders • Improved data processing pipelines and Tableau CRM Dataflows (8 months) Supported the Marketing team in data-related initiatives Reviewed and optimized Alteryx workflows and Tableau dashboards Led workshops to design and implement data integration workflows across systems (SAP, Salesforce, internal sources, etc.)

Consultant – Tableau CRM Deployment • Deployed Einstein Analytics (Tableau CRM) for a client already using Salesforce • Designed and implemented dataflows to integrate various datasets into the platform • Managed unstructured data through custom-built data pipelines • Got Einstein Analytics (Tableau CRM) certified

Final Year Project – PwC France Interpreting Machine Learning Algorithms • Conducted a project focused on understanding and explaining decisions made by Machine Learning models, particularly in the context of auditing and automated decision-making. • Researched and compared various Python libraries for model interpretability, including SHAP, Skope-Rules, LIME, etc. • Implemented these tools on real-world use cases to analyze feature contributions to model predictions. • Applied supervised Machine Learning techniques (decision trees, random forests, gradient boosting) with a focus on transparency and auditability. • Delivered technical documentation and presented findings to non-technical stakeholders.

• Put a Power BI tool in place to help doctors follow the usage of the connected devices. • Back-end development with Java to update Data Model. • Used python and bash basics to implement and run scripts. • Designed first and simple dashboards on AWS Quicksight. • Worked in Agile environment.

• Assisted with stand management during the holiday season • Welcomed and advised customers • Handled cash register operations and payments