Data Scientist

CT Advisor – Data Scientist - ML Engineer

Client - Directorate General for Judicial Affairs

• The Client is a specialised police force, mainly responsible for the fight against organised crime in all its forms. It is one of the three directorates-general of the Client and concentrates investigation files relating to areas such as cybercrime, terrorism, organized crime, drug trafficking, and many others. It provides support and expertise to all integrated policing, as well as its national and international partners.
• Within this Directorate, there are operational resources allocated to judicial police operations, the fight against serious and organized crime, special units, as well as technical and scientific police operations.

• As a data scientist, 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 judicial police. You will be responsible for building machine learning pipelines, as well as monitoring and maintaining them. You will also be responsible for the production of AI models for the organization with mainly on-premise deployments, You will ensure the implementation of the best standards in programming and machine learning within your projects. You will carry out a technology watch in order to keep up to date with the latest developments in the fields of MLOps and machine learning.
• Attention to the safety, ethical and legal aspects is a plus appreciated.

• You have a master's degree or a doctorate in computer science, AI or equivalent and can justify a minimum of 3 years of experience in the fields of data science, MLOps and ML

You have expertise in the following areas:

• On-premise and cloud development: Expertise in the development and deployment of on-premise and cloud (Azure, AWS, GCP) AI solutions.
• 3+ years of experience in the industry: You have 3+ years of experience in the world of ML, MLOps and big data with a focus on large-scale deployment.
• Theoretical background and practical expertise: in the field of ML and deep learning.
• Database Paradigm (SQL & NoSQL): Advanced knowledge of relational and non-relational DB (SQL and NoSQL) including PostGres, Mysql, Milvus, Neo4J, ...
• ML MLOps: Demonstrable experience in deploying ML models and expertise in MLOps.
• Big data focus: experience in working with large structured and unstructured datasets.
• Containerization and deployment: Experience with Docker and Kubernetes as well as orchestration tools such as Kubeflow. Mastery of ML pipelines (Kubeflow, MLflow, SageMaker,..)
• CI/CD for ML: Proficiency in implementing CI/CD for ML models and related code.
• Data Storage: Experience with different solutions for data storage (data lakes, dataware house, object storage (S3))
• System architecture: Ability to designate an end-to-end ML system taking into account scalability, robustness, maintenance and hardware constraints.

Hard Skill

• Databases: MySQL, PostgreSQL, Neo4j, Milvus
• AI Framework: huggingface, mlflow, PyTorch, tensorflow, sklearn, OpenCV, vllm
• Programming Langages : Python (R est un plus)
• Orchestration et containerisation: Docker, Kubernetes, Kubeflow
• Software engineering : uv, ruff, black
• Cloud Platforms : Azure, AWS
• Versioning (code et modèles) : MlFlow, Git, Github, Gitlab

Languages

• Knowledge of languages: You have at least a good command of English and one of the two
• national languages (NL/FR).

Soft Skills

• Ability to federate: Knowing how to align heterogeneous profiles around a common goal.
• Sense of priorities: Identify critical tasks to achieve objectives, maintain a long-term vision to anticipate next steps, and reorganize work according to unforeseen events or new elements that arise during the project. Clear and spontaneous communication: Communicate in a fluid way, convey the right message, at the right time and at the right level. Be able to explain technical concepts to non-technicians.
• Problem solving and analytical thinking: Approach problems in a structured way, identify root causes, and propose pragmatic and effective solutions. Be able to take a step back to evaluate several scenarios and choose the most appropriate solution for the context.
• Collaboration: Work constructively with all stakeholders, promote exchanges and co-construction of solutions. Listen to the needs and constraints of each person to promote a positive and productive work climate.
• Attention to detail: Attention to the technical, functional and organizational aspects of projects. Ensure the quality of the code, the robustness of the templates, the compliance of the deliverables and the respect of the organization's standards.
• Rigor: Systematically apply best practices and methodologies,document work accurately, and ensure constant monitoring of project progressin compliance with deadlines and quality requirements