Vichal Mukam

📊 Project Description:

This project aims to design a real-time data analysis platform dedicated to the performance of Real Madrid and FC Barcelona teams, as well as their players.

Leveraging the API-Football, the team is implementing a modern data architecture (ELT + medallion model) to extract, store, transform, and visualize sports information using BigQuery, DBT, and Looker.

Beyond analysis, the project integrates an intelligent automation system: through the OpenAI API and the X (Twitter) API, messages are automatically generated and published during key live events (goals, cards, substitutions, etc.), ensuring instant communication enriched by AI.

Everything is orchestrated using Docker, Kafka, and technical monitoring via VS Code Monitoring, guaranteeing the solution's reliability and performance.

• Automation of processes related to credit activities and development of specific tools for management and decision-making (Power Automate, Power BI, SharePoint, Excel, Python, Microsoft Lists, JSON)

○ Extraction, cleaning, transformation, and ingestion of data with Python for a dashboard to monitor production using Power BI.

○ Implementation of an automated workflow with Power Automate, significantly reducing process processing time.

○ Creation of a bank statement analysis tool and an automated form for managing client files.

○ Result: Significant time savings and 20% performance increase in file processing.

• Evaluation of advisors by managers on the quality of telephone customer service (Power BI).

○ Lack of visibility on the evaluation of call quality in contact and customer relations centers.

○ Implementation of a dashboard showing the evolution of evaluations in call centers across different markets.

○ Result: Considerable assistance in decision-making for customer contact center directors.

• Performing marketing analyses on customer routing and segmentation via Power BI, contributing to a 60% increase in the number of customers.

• Analysis of business and operational needs, with the development of detailed specifications (functional and technical).

• Adherence to AGILE principles and active participation in SPRINT meetings and project documentation, work summaries, and presentations.

As part of the fiber optic network deployment contract in Normandy for Orange, implemented data science tools to optimize operational performance of construction sites and network quality.

• Exporting, preparing data, and creating visualizations for monitoring technician activities at different operational sites.

○ Centralizing, cleaning, and structuring datasets via Power Query (Excel/Power BI) and Python (via VS Code or Azure Notebook).

○ Creating dynamic dashboards with Power BI for monitoring construction progress, analyzing compliance rates, and detecting critical delays and alerts.

○ Connecting Power BI to SharePoint Lists and OneDrive for Business for automatic data source updates.

○ Result: Significant time savings, 20% performance increase, and more effective decision-making for management.

• Modeling & Machine Learning for developing predictive models in Python.

○ Linear regression and Random Forest for predicting construction durations at different sites in the Normandy region.

○ Binary classification/XGBoost for detecting non-compliance risks during operations according to standards.

○ K-Means clustering for mapping intervention area typologies.

○ Result: Considerable contribution to the efficiency of work on construction sites and optimization of technician work planning.

• Analysis of business and operational needs, with the development of detailed specifications (functional and technical).

• Communication and technological popularization to make technical concepts more accessible to business units and management.

• Adherence to AGILE principles and active participation in SPRINT meetings and project documentation, work summaries, and presentations.