Imen Bali

Project: Implementation of a Decision Support System for the direction of BK Food Tunisia in collaboration with EY Tunisia to monitor Tuna Sales Activity :

• Identify organizational needs of commercial domain, marketing, finance, management control, workflows and collaborators objectives Coordinate the acquisition, automation, cleansing, and structuring of diverse data sources (commercial domain, marketing, finance...) essential for valuation purposes.

• Collaborate closely with the Finance & Valuations team to deliver data-driven insights and actionable recommendations.

• Aid in crafting comprehensive financial reports, presentations, and valuation documents.

• Create data transformation workflows that clean, normalize, and transform the data as needed.

• Create interactive dashboards for streamlined data reporting and in-depth analysis.

• Propose innovative enhancements and solutions to optimize existing data processes and models.

• Actively participate in team meetings to foster a collaborative culture of knowledge sharing and continuous learning.

Project: Real-time data analysis of virtual eyewear fitting solutions (Client's solution: Fitting Box augmented reality):

• Functional and technical analysis of existing systems.

• Understanding data sources.

• Implementation of ETL processes.

• Validation of data quality.

• Implementation of simple and complex data transformations using DataBrew.

• Data storage on Redshift.

• Crossing and modeling of data.

• Implementation of dashboards using QuickSight.

• Writing technical documentation.

Technical environment: AWS, S3, Databrew, Redshift, Quicksight, ETL, SQL, EXCEL

Project: Implementation of an intelligent automation solution for the recruitment process:

• Writing functional and technical specifications.

• Designing and modeling the database required for extracting data from resumes.

• Extracting data from resumes using NLP techniques (Spacy, NLTK...) and Python libraries (PyMuPDF,...).

• Validating the quality of extracted data.

• Automating data ingestion into the database by implementing a PL/SQL script.

• Implementing ETL processes.

• Crossing and modeling data.

• Implementing dashboards using Power BI.

• Deploying the solution with Docker.

Technical environment: Python, PyMuPDF, PyPDF2, NLP, Spacy, NLTK, PLSQL, Power BI, Docker, Git.

Project: Tracking and analysis of data from an e-services website:

• Writing functional and technical specifications.

• Ingestion of website events with Segment.

• Implementation of ETL.

• Implementation of data transformations using Python.

• Storage of data on BigQuery.

• Implementation of dashboards with DataStudio.

Technical environment: GCP, Python, Segment, BigQuery, DataStudio.

• Migration of the existing technical architecture (from ElasticSearch, Athena, and Kibana) to S3, Redshift, Databrew, and Quicksight.

• Understanding of data sources.

• Implementation of ETL processes.

• Implementation of simple and complex data transformations with DataBrew.

• Storage of data on Redshift.

• Crossing and modeling of data.

• Implementation of new dashboards with QuickSight.