À propos de Hassen
Machine Learning Engineer with Data Science and Data Engineering experience in buiding anomaly detection and fraud prevention systems, overcoming complex architectural and scalability issues in Banking industry. I have practiced Machine Learning and statistical techniques at high scale using tools like Spark, Hadoop and Python, Mlflow and Airflow. I have designed production data pipelines to prepapre, train and deploy Machine Learning models. I am comfortable with production grade code in either Python and Java.
Arabe
Bilingue ou natif
Français
Bilingue ou natif
Anglais
Capacité professionnelle complète
Accepte de travailler sur site
Paris (jusqu’à 50 km)
Expériences
- Swiss Life FranceLead Machine Learning Engineerfévrier 2022 - Aujourd'hui (4 ans et 4 mois)Building AI systems (Frauds, antichurn, NLP...) in the transversal Data Science & AI team in collaboration with SwissLife entities & strategic partners, and implementing MLOps best practices tools, and Machine Learning & Deep Learning models.
- BNP PARIBASLead Machine Learning Engineerseptembre 2020 - octobre 2022 (2 ans et 1 mois)- Lead the maintenance, development, optimization and research of new Machine Learning models for fraud detection at scale (datasets up to billions of rows).- Responsible of the Data Lab Flux platform: maintenance, implementation of new technologies and actively contribute to the evolution of the platform.- Ensure the quality of the code, factorization and documentation. Optimize and enhance computational efficiency of algorithms and software design.- Guide non-technical team in understanding analytics at scale, infrastructure as code and best practices for robust software development.- Implementation and continuous improvement of several production pipelines: Data extraction, Train, experiments, industrialization and monitoring.- Partner with business stakeholders, engineering team and Data Scientists in order to add/implement new features for fraud detection models.- Lead the Warmup process(using historical data, the Warmup is used to initialize the features used in fraud detection): implementation and industrialization. Technologies: Java, Python3, Spark, Flink, Apache Kafka, Hadoop, Airflow, MlFlow, JupyterHub, Machine Learning Libraries (pandas, sklearn, Tensorflow, shap…), GitLab, Jenkins, Ansible, Splunk.
- BNP PARIBASData Engineer/Scientistoctobre 2018 - septembre 2020 (1 an et 11 mois)Île-de-France, FranceAt OLAF (Outil Lutte Anti Fraudes) project, our mission is to build a real time platform to detect and prevent fraud with Machine Learning, directly in the payment systems before any money went out:- Work closely with the risk team and Data Architects in order to construct the first Data Lab Flux.- Develop a software layer in Python (based on MlFlow & Airflow) for the the non-technical Data Science Team to bring automation to the model design and experimentation processes.- Partner with the business team, data engineers and data scientists in order to design the first machine learning models for fraud detection.- Ensure the optimization and the continuous improvement of Machine Learning models for fraud detection: Contribute with the business team to identify new features, Sampling techniques, Split strategy (Time series), Bayesian optimization of scenarios (business rules and ML). Technologies: Java, Python3, Spark, Flink, Apache Kafka, Hadoop, Airflow, MlFlow, Jupyter, Machine Learning Libraries (pandas, sklearn, Tensorflow, shap ...)
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Formations
- Master M2 Informatique décisionnelleUniversité Paris Dauphine2017Master M2 Informatique décisionnelle
- Diplôme national en ingénieurie informatiqueÉcole nationale des sciences de l'informatique2015Diplôme national en ingénieurie informatique