DATA SCIENTIST
DATA SCIENTIST
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Job Description
Job Description
This position comprises two complementary aspects: the coordination of AI and data science projects and the hands-on implementation of related technological solutions.
As coordinator you will serve as the central reference point within the organization for the deployment of AI initiatives. You will collaborate closely with IT and business stakeholders to translate operational needs into feasible and scalable AI/data projects. You will lead the technical aspects of these initiatives overseeing all phases including analysis development industrialization monitoring and maintenance. While you will not always act as the formal project manager you will supervise junior and mid-level technical staff involved in the projects under your guidance.
On the implementation side you will contribute directly to the design and development of AI and data science solutions tailored to the organization s operational and tactical requirements. You will be responsible for building deploying and maintaining machine learning pipelines ensuring they are monitored effectively. As a senior technical profile you will promote best practices in programming and machine learning across the team while staying up to date with the latest developments in MLOps and machine learning. You will also provide technological guidance to your peers supporting them in strategic and technical decision-making.
Requirements
You hold a Master s or PhD in computer science artificial intelligence or a related field and you have at least 5 years of relevant experience in data science MLOps and machine learning.
You possess strong expertise in the following areas:
On-Premise and Cloud Development: Experience in developing and deploying AI solutions both on-premise and on cloud platforms (Azure AWS GCP).
Professional Experience: Minimum of 5 years working with machine learning MLOps and big data technologies.
ML & Deep Learning: Strong theoretical background and hands-on experience in machine learning and deep learning.
Database Paradigms: Solid knowledge of relational and non-relational databases (SQL and NoSQL) including PostgreSQL MySQL Milvus and Neo4j.
ML & MLOps Deployment: Proven experience in deploying ML models and implementing robust MLOps practices.
Big Data Handling: Experience working with large structured and unstructured datasets.
Containerization & Deployment: Experience with Docker and Kubernetes and with orchestration tools like Kubeflow. Familiarity with ML pipeline tools such as Kubeflow MLflow SageMaker.
CI/CD for ML: Mastery of CI/CD workflows tailored to machine learning code and models.
Data Storage: Familiarity with data lakes data warehouses and object storage solutions (e.g. S3).
System Architecture: Ability to design full ML systems end-to-end with a focus on scalability robustness maintainability and infrastructure constraints.
Hard Skills
Databases: MySQL PostgreSQL Neo4j Milvus
AI Frameworks: PyTorch TensorFlow HuggingFace scikit-learn OpenCV MLflow
Programming Languages: Python (R is a plus)
Cloud Platforms: Azure AWS
Orchestration Tools: Kubernetes Kubeflow
Version Control (code & models): Git GitHub GitLab DVC MLflow Neptune
Languages
You are fluent in English and have strong proficiency in at least one of the two national languages (French or Dutch).
Employment Type
Full Time
