About the mission
We are looking for a Data Scientist to join our team, focusing on the development of AI and Data Science solutions for the judicial police. The ideal candidate will have a master's or doctorate in computer science or AI, with at least 3 years of experience in data science and MLOps, and expertise in machine learning, cloud development, and big data.
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Description
ICT Advisor – Data Scientist - ML EngineerThe 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 areas such as cybercrime, terrorism, serious organized crime, drug trafficking, and many others. It provides support and expertise to the entire integrated police force, as well as to its national and international partners.
Key Responsibilities
As a data scientist, you will directly participate in the design and development of AI and Data Science solutions aimed at meeting 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 deploying AI models for the organization, with deployments primarily on-premise. You will ensure the implementation of the best standards in programming and machine learning within your projects. You will keep abreast of the latest developments in MLOps and machine learning. Attention to security, ethics, and legal aspects is a plus.Skills & Experience
You hold a master's or doctorate in computer science, AI, or equivalent and can demonstrate 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 in the cloud (Azure, AWS, GCP).
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 in 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.
Focus on big data: experience in handling large structured and unstructured datasets.
Containerization and deployment: Experience with Docker and Kubernetes as well as orchestration tools like Kubeflow. Mastery of ML pipelines (Kubeflow, MLflow, SageMaker, ..)
CI/CD for ML: Mastery of 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 considering scalability, robustness, maintenance, and hardware constraints.
Languages
Language knowledge: You master at least English as well as one of the two national languages (NL/FR).Additional Info
Hard Skill
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
Soft Skills
Ability to unite: Know 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 based on unforeseen circumstances or new elements that arise throughout the project.
Clear and spontaneous communication: Communicate fluently, 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 manner, identify root causes, and propose pragmatic and effective solutions. Be able to step back to evaluate multiple scenarios and choose the most suitable solution for the context.
Collaboration: Work constructively with all stakeholders, promote exchanges and co-construction of solutions. Be attentive to the needs and constraints of everyone to foster a positive and productive work environment.
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 while respecting deadlines and quality requirements.
What you'll do
- Participate in the design and development of AI and Data Science solutions
- Develop machine learning pipelines and monitor their performance
- Deploy AI models for the organization with on-premise deployments
- Implement best standards in programming and machine learning
- Stay updated with the latest developments in MLOps and machine learning
What we're looking for
- Master's or doctorate in computer science, AI, or equivalent
- Minimum of 3 years of experience in data science, MLOps, and ML
- Expertise in on-premise and cloud development (Azure, AWS, GCP)
- Experience with large-scale deployment in ML and big data
- Advanced knowledge in relational and non-relational databases (SQL and NoSQL)
- Demonstrable experience in deploying ML models and expertise in MLOps
- Experience in handling large structured and unstructured datasets
- Experience with Docker and Kubernetes
- Mastery of ML pipelines (Kubeflow, MLflow, SageMaker)
- Mastery of implementing CI/CD for ML models
- Experience with various solutions for data storage (data lakes, data warehouse, object storage)
- Ability to design an end-to-end ML system considering scalability and robustness
Skills
- Azure
- AWS
- Docker
- Kubernetes
- Kubeflow
- MLflow
- MySQL
- PostgreSQL
- Neo4j
- Milvus
- Python
- PyTorch
- TensorFlow