Data Scientist

Job Title: Data Scientist

Language: French, Dutch, and English

Location: 1000 Brussels, Belgium

Duration: 1/07/2025 to 31/12/2025 + Possible extension

On-Site position

Job description:

• This role includes two complementary aspects: on the one hand, an aspect relating to the coordination of AI and Data Science projects, and on the other hand, the provision of technical expertise in the context of the implementation of solutions.
• Regarding coordination, you will be the reference person within the organization for the implementation of AI initiatives. You will collaborate closely and regularly with other IT and business profiles within the organization to translate operational needs into viable projects. In this context, you will also provide technical leadership on the AI ​and Data Science aspects of the projects in which you participate by supervising all phases of the projects, analysis, development, industrialization, monitoring and maintenance. You will also supervise the junior and medior technical profiles involved in the projects under your responsibility without, however, systematically taking on the role of project manager.O
• n the implementation side, you will be directly involved in the design and development of AI and Data Science solutions designed to meet the operational and tactical needs of the client. You will be responsible for developing machine learning pipelines, as well as monitoring and maintaining them. As a senior member, you will ensure the implementation of the best programming and machine learning standards within the team. You will conduct technology monitoring to stay up to date with the latest developments in the fields of MLOps and machine learning, and will be able to advise your colleagues on their technological and programmatic choices.D

esired Profile:Y

• ou hold a master's or doctorate in computer science, AI or equivalent and can demonstrate a minimum of 5 years of experience in the fields of data science, MLOps and MLY

ou have expertise in the following areas:O

n-premise and cloud development:E

• xpertise in the development and deployment of AI solutions on-premise and in the cloud (Azure, AWS, GCP).5
• + years of industry experience: You have 4+ years of experience in the world of ML, MLOps and big data.T
• heoretical background and practical expertise: in the field of ML and deep learning.D
• atabase Paradigm (SQL & NoSQL): Advanced knowledge of relational and non-relational DB (SQL and NoSQL) including PostGres, Mysql, Milvus, Neo4J, etc.M
• L MLOps: Demonstrable experience in deploying ML models and expertise in MLOps.B
• ig data focus: experience in exploiting large sets of structured and unstructured data.C

ontainerization and deployment:E

• xperience with Docker and Kubernetes as well as with orchestration tools like Kubeflow. Mastery of ML pipelines (Kubeflow, MLflow, SageMaker, etc.)C
• I/CD for ML: Proficiency in implementing CI/CD for ML models and associated code.D
• ata Storage: Experience with different solutions for data storage (data lakes, dataware house, object storage (S3))S
• ystem architecture: Ability to design an end-to-end ML system taking into account scalability, robustness, maintenance and hardware constraints.H

ard Skils:D

• atabases: MySQL, PostgreSQL, Neo4j, MilvusA
• I Framework: PyTorch, tensorflow, huggingface, mlflow, sklearn, OpenCVP
• rogramming Languages: Python (R is a plus)D
• atabases Paradigms: NoSQL, SQLO
• rchestration: Kubernetes, KubeflowC
• loud Platforms : Azure, AWSV
• ersioning (code et modèles) : MlFlow, dvc, Neptune, Git, Github, GitlabS

oft Skills:A

bility to unite: Knowing how to align heterogeneous profiles around a common objective.S

• ense of priorities: Identify critical tasks to achieve objectives, maintain a long-term vision to anticipate next steps, and reorganize work based on unforeseen events or new elements that arise during the project.C
• lear and spontaneous communication: Communicate fluently, conveying the right message, at the right time and at the right level. Be able to explain technical concepts to non-technical people.P
• roblem solving and analytical thinking: Approach problems in a structured manner, identify root causes, and propose pragmatic and effective solutions. Be able to step back to evaluate multiple scenarios and choose the solution best suited to the context.C
• ollaboration: Work constructively with all stakeholders, fostering discussion and the co-construction of solutions. Listening to each person's needs and constraints to foster a positive and productive working environment.A
• ttention to detail: Pay attention to the technical, functional, and organizational aspects of projects. Ensure code quality, model robustness, deliverable compliance, and adherence to organizational standards.R
• igor: Systematically apply best practices and methodologies, document work accurately, and ensure constant monitoring of project progress in compliance with deadlines and quality requirements. S

kills:A

zure DVC Huggingface Kubernetes MIFlow KiteMLflow MySQL Neo4J Neptune NoSQL PostgreSQL Python PyTorch SQL TensorFlow