oussama chaker

Project environment is Cloudera CDP 7 : Participation in our client technical scoping workshops which resulted in : Data collection and ingestion to Datalake (Hdfs). Data Feeding of the Datawarhouse (Apache Hive) to perform data enrichment from different business areas in the banking sector. Data Transformation with Apache Spark(Scala API). Build a Data warehouse: Build a data warehouse house using Talend BIGDATA to obtain in a single view all of the bank’s financial axes. data ingestion from different sources to postgres and MongoDB data mapping data updates data enrichment Managing changes in dimensions is an issue of historization in the data warehouse Implement a project using GCP : The objective is to track our client customer behavior for internal usage. It consists in the collection, measurement, analysis and visualization of the data related to the user’s interactions with our client digital channels (Website, mobile app). These processes are elaborated with Big query and Tag manager (Google Cloud Platform).

I contributed to the implementation of a BIG DATA and IA solutions that responds to the need of clients. Technologies used : Flink, Scala, Kafka, JAVA, Kafka Connect,Python, opencv, TensorFlow, Azure Data

Back to FIS for a 3rd experience , once again, a very interesting project "intelligent automation of the project process " that will serve me greatly to validate the career choice I was looking to make in the field of artificial intelligence and the data science.

We implemented a proof of concept that combines fingerprint recognition, facial recognition, voice recognition and behavioural authentication to secure access to a platform. My main task was to design and develop a micro service that analyzes the user's behavior during authentication. The purpose of this micro-service is to keep the application highly secure and user-friendly at the same time. The behaviors adopted were decided after a survey on common behaviors among the employees of FIS Tunisia. One of the results of the study was the location of the user. Thanks to grouping algorithms, I implemented a solution that indicates whether the user connects from his usual location or not. The result is evaluated by the application to finally decide which security operations to request.

Design and Development Project: A framework for Real-time traffic data acquisition and monitoring architecture for automated