Mohamed B.

→ Mission Objective:

Design, industrialize, and operate data pipelines to provide reliable analytics, actionable dashboards, and automated tools that meet the needs of business and decision-making teams.

→ Achievements:

Close interaction with internal teams to understand business needs and translate business challenges into actionable data solutions → better alignment between data and decision-making.

Ingestion and processing of data from multiple sources (databases, cloud, files) using AWS, GCP, Python, SQL, and Snowflake → large-scale data centralization and reliability.

Design, development, and maintenance of automated data pipelines → continuous generation of insights and reduction of manual processing.

Development of automated dashboards and analytical solutions → improved analysis efficiency and time savings for internal teams.

Creation of clear and educational visualizations (Tableau, Power BI, Matplotlib, Seaborn, Plotly) → simplification of complex analyses for non-technical audiences.

Facilitation of agile rituals (daily stand-ups), task tracking, and blocker resolution via JIRA → smooth project progress and effective communication with stakeholders.

Training and support for users in dashboard creation and Snowflake usage → upskilling of teams and cost reduction related to bad practices.

Implementation of CI/CD practices and code management via Git and Azure DevOps

→ more robust, maintainable, and industrialized data pipelines.

→ Technical Stack:

Python, SQL, Snowflake, AWS, GCP, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Plotly, Tableau, Power BI, Git, Azure DevOps, JIRA.

→ Mission Objective:

Design and deploy automated cloud data solutions to provide reliable and usable data sources for Analytics teams and internal clients, supporting data-driven decision-making.

→ Achievements:

Development and deployment of solutions on Google Cloud Platform (Cloud Functions, BigQuery) → automation of data collection and processing from multiple APIs.

Implementation of reliable and documented data sources in close collaboration with the Analytics and business teams → improved knowledge sharing and data utilization.

Management and utilization of data entered by users via Google Firebase (web and mobile) → consistency of multi-platform data.

Collaborative work via Git → version management, development organization, and continuous improvement of data solutions.

→ Technical Stack:

Google Cloud Platform (Cloud Functions, BigQuery), APIs, Google Firebase, Git, Spark, Google Analytics, AWS, Data Engineer, Python

→ Mission Objective:

Identify operational practices with the highest impact on sales growth, profitability, and customer reviews, using structured and unstructured data, to guide the group's strategic decisions.

→ Achievements:

Correlation analysis and predictive modeling on audit and sales data from all Pizza Hut restaurants in the UK → identification of key drivers of commercial performance.

Development of sales forecasting models using time series with XGBoost → improved forecast accuracy.

Hyperparameter optimization via genetic algorithms → enhanced model performance and robustness.

Text analysis and topic modeling on customer reviews → in-depth understanding of factors influencing customer satisfaction and perception.

Implementation of Explainable AI (XAI) techniques → clear interpretation of results and adoption by business teams.

→ Results:

Production of a prioritized list of behaviors and practices with the highest impact on sales growth, profitability, and customer reviews, as well as highlighting the relationships between operational practices and business performance.

→ Technical Stack:

Python, XGBoost, Genetic Algorithms, NLP / Topic Modeling, Time Series Analysis, XAI, Data Science, SQL, GCP, NLP