À propos de Slim
Having over 7 years of extensive experience in the realm of Data, I am passionate about engaging in impactful projects.
My journey at renowned companies such as L'Oréal, Servier, and Société Générale has provided me with a profound understanding of all aspects of Data projects, encompassing Data collection, cleansing, storage, KPI development, algorithmic design, AI model development, and seamless transition into production environments. Furthermore, I have refined my abilities in team management and communication of insights to stakeholders.
My passion lies in tackling customer challenges head-on and crafting innovative solutions using state-of-the-art technology. Engaging with diverse individuals and embracing fresh perspectives invigorates me. I thrive to conceive, scale, and refine models that matters. To accomplish this, I prioritize delivering key outcomes and adapting to new learnings.
Technical Stack:
• Cloud Production on GCP/AWS/Azure
BigQuery/SnowFlake/DBT (SQL), Storage/Buckets, Compute/EC2, IAM, Dataflow, Workflows, Schedulers, Pub/Sub, CloudRun for app Deployement, Cloud Build for CICD, Docker/Kubernetes, Terraform and Git
• Machine Learning/Deep Learning
Pytorch/TensorFlow/Keras, Xgboost, Sklearn + GCP VertexAI/Amazon SageMaker
• Database
BigQuery/SnowFlake/DBT (SQL), Firestore/MongoDB, Elasticsearch (NoSQL DB) and Neo4j (Graph DB)
• Visualisation
PowerBI, Looker Studio, Tableau, Kibana
Français
Bilingue ou natif
Anglais
Capacité professionnelle complète
Accepte de travailler sur site
Paris (jusqu’à 15 km)
Expériences
- L'Oréal SA - L'Oréal FranceSenior Data Engineer on GCPMODE & COSMÉTIQUESjanvier 2022 - Aujourd'hui (4 ans et 5 mois)Paris, FranceDedicated to empowering over 1000+ business key users across multiple zones and countries by driving strategic business decisions through the implementation of advanced AI Solutions and seamless Data Deliveries.• Led the development of project focused on enhancing delivery efficiency, leveraging data to analyze on-time full rates KPIs.• Developed and industrialized in production a custom-tailored financial scoring algorithm in production, analyzing financial results, assigning scores, and ranking them to support financial decision-makers.• Designed and deployed in production a deep learning time series model solution predicting next year's financial results across multiple scenarios, managing over 200,000+ time series concurrently.• Delivered key presentations of results to stakeholders, ensuring clear communication of findings and insights.• Architected Projects infrastructure from scratch using Terraform and provisioning all GCP cloud resources.• Established Cloud Build pipelines for resource deployment and using Git for version control.• Designed data schema architectures, enabling the implementation of daily workflows for complex data pipelines handling terabytes of data using BigQuery.• Managed cloud resources and orchestrated pipelines utilizing Cloud Workflow, Airflow, and Cloud Scheduler.• Oversaw developers within the team, establishing best practices policies, conducting code reviews, and ensuring thorough documentation.Technical Stack:• Google Cloud Platform (BigQuery, Workflows, etc ..) for Cloud Production• Cloud Production deployement using Python, Docker, Terraform, Cloud Build, and Git.• Machine Learning / Deep Learning in Python using Keras/Tensorflow along with BIGQUERY ML and Vertex AI fort Cloud deployement.
- Servier PharmaceuticalsSenior Data Scientist / Data Engineer on GCPSANTÉ & BIEN-ÊTREmai 2020 - décembre 2021 (1 an et 8 mois)Paris, France• As a member of the Data Factory team, I designed and implemented Data Pipelines within an industrialized environment to handle extensive medical databases, exceeding terabytes in size. These pipelines were constructed on BigQuery and GCP Data Lake, utilizing Apache Beam for distributed batch and stream processing tasks.• Constructed a comprehensive Graph-based representation containing millions of data nodes and interconnected links spanning drugs, proteins, genes, and diseases within Neo4j.• Developed Docker Images and orchestrated their deployment on a Kubernetes cluster. Additionally, I implemented a monitoring tool for applications to assess metrics and analyze logs effectively.• Enhanced CI/CD Pipelines by integrating Unit & Integration tests and enforcing code formatting according to best practices.• Used Terraform for deploying resources on Google Cloud Platform (GCP).• As part of the AI Team, Teamed up with OWKIN's Data Science experts and the Servier Immuno-Inflammation department to jointly develop a cutting-edge Deep Learning-powered 3D-Image Recognition solution. Our collaborative efforts targeted MRI scans, aiming to advance preventive measures against knee osteoarthritis progression.• Engaged in the development of Deep Learning models tailored for predicting drug combinations based on graph data structures.• Responsible for delivering presentations of results to stakeholders and researchers within the Servier Immuno-Inflammation department.Technical Stack:- Google Cloud Platform (BigQuery, Dataflow, Cloud Build/CICD, Docker, Kubernetes, Terraform and Git) for Cloud Production- Deep Learning in Python using Keras/Tensorflow + Vertex AI- GCS, BigQuery, Elasticsearch (NoSQL DB) and Neo4j (Graph DB)
- TransdevData Scientist / Data Engineer on AWSTRANSPORTSmai 2019 - avril 2020 (11 mois)Paris, France• As a member of the Data Lab team, I took charge of deploying industrialized products on AWS. This encompassed the implementation of Customer Segmentation, Churn Classification, and Forecasting models, serving a vast user base of over 100,000 individuals.• Developed an innovative Dynamic Public Transport Stream Forecasting solution leveraging origin-destination data, categorical information, geolocation data from users, and bus drivers' logs.• Developed Docker Images and orchestrated their deployment on a Kubernetes cluster. Additionally, I implemented a monitoring tool for applications to assess metrics and analyze logs effectively.• Enhanced CI/CD Pipelines by integrating Unit & Integration tests and enforcing code formatting according to best practices.• Oversaw the guidance and supervision of new trainees, alongside facilitating the training of incoming team members.Technical Stack:- AWS (Storage, EC2, IAM, Lambda, Docker for Containerization and Git) for Cloud Industrialization- Pytorch, Keras (Deep Learning)- PowerBI, Tableau, Flask (Visualisation)
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Formations
- Master's degreePierre and Marie Curie University2018Statistics, Machine Learning and Deep Learning
- Bachelor's degreePierre and Marie Curie University2016Mathematics and Computer Science
Certifications
- Deep Learning SpecializationCoursera2019
- TOEIC (980/990)TOEIC® Program2017