About 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.
Arabic
Native or bilingual
French
Native or bilingual
English
Fluent
Can work on-site
Paris (up to 50km)
Experience
- Swiss Life FranceLead Machine Learning EngineerFebruary 2022 - Today (4 years and 4 months)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 EngineerSeptember 2020 - October 2022 (2 years and 1 month)- 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/ScientistOctober 2018 - September 2020 (1 year and 11 months)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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Education
- Master M2 Informatique décisionnelleUniversité Paris Dauphine2017Master M2 Informatique décisionnelle
- Diplôme national en ingénieurie informatiqueÉcole nationale des sciences de l'informatique2015Diplôme national en ingénieurie informatique