Housni Hassani

At Global Praxis, I work as a Data Scientist on strategic consulting missions based on advanced market data analysis. My role covers the entire data cycle, from collection to modeling, with a strong business focus.

Main missions:

Analysis of market and business data (sales, visits, market shares) cross-referenced with external data (demographics, economics, geography)

Creation and management of databases on MySQL (hosted on AWS), interfaced with Excel, Power BI, or R Shiny

Data engineering: structuring, cleaning, and enriching data to make it accessible to business teams

Development of custom tools:

Dynamic questionnaires (SurveyJS)

Interactive dashboards (Excel, Power BI)

Web applications for reporting results (R Shiny)

Statistical modeling and machine learning: segmentation, clustering, ROI-oriented predictive analysis

Reporting of insights in a strategic support logic: cost optimization, improvement of commercial performance

🔧 Technical Environment:

R, SQL, Power BI, Excel (Power Query & Power Pivot), R Shiny, KNIME, Git, Python, Alteryx, SurveyJS, AWS (MySQL)

As a data analyst, my responsibilities include collecting, cleaning, organizing, and analyzing data to meet the needs of decision-makers. I am also responsible for developing dashboards, reports, and visualizations to communicate insights to stakeholders, as well as using advanced data analysis techniques to uncover trends, patterns, and relationships in the data.

I am also responsible for applying statistical and modeling methods to solve complex problems. As a key member of the team, I am involved in developing the data strategy and am responsible for its implementation. By working closely with other teams, including development and project management teams, I ensure that data is collected and used appropriately to meet business needs.

- Data preprocessing and collection from various types of databases from different sources

- Data processing and analysis

- Dashboard creation

- Algorithm construction

- Proposal of recommendations to improve the data analysis and processing process