~26d ago

Data Scientist Consultant

Belgium·Brusselsmid

Data ScientistData

4 views0 saves0 applied

Quick Summary

Key Responsibilities

Design and develop AI and Data Science solutions for operational needs. Develop and maintain machine learning pipelines. Deploy AI models primarily on-premise.

Requirements Summary

You hold a master's or PhD in computer science, AI, or a related field and have a minimum of 3 years of experience in data science, MLOps, and ML.

Technical Tools

Data ScientistData

As a Data Scientist, you will be engaged in a consultancy mission at a client site representing Keystone Solutions. You will directly participate in the design and development of AI and Data Science solutions to meet the operational and tactical needs of the judicial police. Your responsibilities will include the development of machine learning pipelines, as well as their monitoring and maintenance. You will also be responsible for deploying AI models for the organization, primarily on-premise, ensuring the implementation of best practices in programming and machine learning within your projects. Staying updated with the latest advancements in MLOps and machine learning will be essential. Attention to security, ethics, and legal aspects is a valued plus.

Responsibilities

~1 min read

→Design and develop AI and Data Science solutions for operational needs.
→Develop and maintain machine learning pipelines.
→Deploy AI models primarily on-premise.
→Implement best programming and machine learning standards.
→Conduct technological watch to stay updated on MLOps and machine learning advancements.

Requirements

~1 min read

You hold a master's or PhD in computer science, AI, or a related field and have a minimum of 3 years of experience in data science, MLOps, and ML.

You possess 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 industry experience: Experience in 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): Advanced knowledge in relational and non-relational databases (SQL and NoSQL) including PostGres, MySQL, Milvus, Neo4J, etc.
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, etc.).
CI/CD for ML: Mastery of implementing CI/CD for ML models and associated code.
Data Storage: Experience with various data storage solutions (data lakes, data warehouses, object storage (S3)).
System architecture: Ability to design an end-to-end ML system considering scalability, robustness, maintenance, and hardware constraints.

Dutch or French
Level Active knowledge
English
Level Active knowledge

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

You are proficient in at least English and one of the two national languages (NL/FR).

Ability to unite: Align diverse profiles around a common goal.
Sense of priorities: Identify critical tasks to achieve objectives, maintain a long-term vision, and reorganize work as new elements arise.
Clear and spontaneous communication: Communicate fluently, convey the right message at the right time and level, and explain technical concepts to non-technical stakeholders.
Problem-solving and analytical thinking: Approach problems systematically, identify root causes, and propose pragmatic and effective solutions.
Collaboration: Work constructively with all stakeholders, fostering exchanges and co-construction of solutions.
Attention to detail: Focus on technical, functional, and organizational aspects of projects, ensuring code quality, model robustness, and compliance with 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 We Offer

~1 min read

At Keystone Solutions, you will experience a dynamic consultancy environment where you will work on diverse projects across various client settings. Our commitment to turbo-charged learning ensures that you will have ample opportunities for professional development and broad learning experiences. As a

If you are ready to tackle technical and strategic challenges in a dynamic consultancy environment, apply today at Keystone Solutions Career Portal.