ICT Advisor – Data Scientist – ML Engineer
Federal Police – General Judicial Directorate

Working Environment

The Federal Judicial Police is a specialized police force, primarily responsible for combating organized crime in all its forms. It is one of the three general directorates of the federal police and focuses on investigation files related to domains such as cybercrime, terrorism, organized crime, drug trafficking, and many others. It provides support and expertise to the integrated police as a whole, as well as to its national and international partners.

Within this directorate are the operational resources assigned to judicial police operations, the fight against serious and organized crime, special units, as well as technical and scientific police operations.

Job Description

As a data scientist, you will be directly involved in the design and development of AI and Data Science solutions to meet the operational and tactical needs of the judicial police. You will be responsible for developing machine learning pipelines, as well as their monitoring and maintenance. You will also be responsible for putting AI models into production within the organization, with deployments mainly on-premise. You will ensure the implementation of best standards in programming and machine learning within your projects. You will carry out technological watch activities to stay up to date with the latest developments in MLOps and machine learning.

Attention to security, ethics, and legal aspects is a valued asset.

Desired Profile

You hold a master's or doctoral degree 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 developing and deploying AI solutions on-premise and on the cloud (Azure, AWS, GCP).
• 3+ years of industry experience: You have 3+ years of experience in the field of ML, MLOps, and big data with a focus on large-scale deployment.
• Theoretical background and practical expertise: in ML and deep learning.
• Database Paradigm (SQL & NoSQL): In-depth knowledge of relational and non-relational databases (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 exploiting 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: Proficient in implementing CI/CD for ML models and associated code.
• Data Storage: Experience with various solutions for data storage (data lakes, data warehouse, object storage (S3))
• System architecture: Ability to design an end-to-end ML system taking into account constraints of scalability, robustness, maintenance, and hardware.

Hard Skills

• Databases: MySQL, PostgreSQL, Neo4j, Milvus
• AI Framework: huggingface, mlflow, PyTorch, tensorflow, sklearn, OpenCV, vllm
• Programming Languages: Python (R is a plus)
• Orchestration and containerization: Docker, Kubernetes, Kubeflow
• Software engineering: uv, ruff, black
• Cloud Platforms: Azure, AWS
• Versioning (code and models): MlFlow, Git, Github, Gitlab

Languages

Language skills: You have at least proficiency in English as well as one of the two national languages (NL/FR).

Soft Skills

• Ability to federate: Able 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 smoothly, convey the right message at the right time and at the right level. 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. 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 party to foster a positive and productive working climate.
• Attention to detail: Pay attention to the technical, functional, and organizational aspects of projects. Ensure code quality, model robustness, compliance of deliverables, and adherence to organizational standards.
• Rigor: Systematically apply best practices and methodologies, document work precisely, and ensure constant monitoring of project progress in compliance with deadlines and quality requirements.