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Je souhaite recevoir la dernière alerte Emploi pour les postes de ce type : team lead data & cloud engineering (brussels, bru)

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BE - Senior Data Science Engineer

Collaboration Betters the World -
Bruxelles

Soumettre votre candidature

Détails de l'emploi

Temps plein

Qualifications

Français
TensorFlow
Informatique
Azure
Doctorat
Informatique
Kubernetes
PyTorch
Vision par ordinateur
Spark
R
Anglais
Google Cloud Platform
Java
Master
SQL
AWS
Docker
Machine learning
Scala
Deep learning
NLP
Développement logiciel
Agile
Kafka
Data science
Architecture de solutions
Intelligence artificielle
Leadership
Compétences en communication
Data warehouse
Python

Description complète du poste

BE - Senior Data Science Engineer

Location Belgium, Brussels

Overview

Overview

We are seeking a highly skilled and experienced Senior Data Science Engineer to join our "Data & AI" service line at CBTW. In this role, you will play a critical role in designing, implementing, and deploying advanced data science and machine learning solutions for our European clients. You will work at the intersection of data engineering, machine learning, and software engineering to deliver scalable, production-ready AI solutions.

You will lead end-to-end data science projects, from problem definition and data exploration to model development, deployment, and monitoring. You will collaborate with cross-functional teams including data engineers, software engineers, and business stakeholders to create innovative AI-driven solutions that deliver measurable business value. As a senior member of the team, you will also mentor junior data scientists and drive best practices in MLOps and model lifecycle management.

Responsibilities

Key Responsibilities

Data Science and Machine Learning

Design and develop advanced machine learning models for various use cases including predictive analytics, recommendation systems, natural language processing, and computer vision
Conduct thorough data exploration and analysis to identify patterns, trends, and insights
Implement feature engineering and selection techniques to optimize model performance
Ensure model interpretability and explainability for business stakeholders

MLOps and Model Deployment

Design and implement end-to-end MLOps pipelines for model training, validation, and deployment
Establish automated model monitoring and retraining workflows
Implement A/B testing frameworks for model performance evaluation
Ensure models meet production requirements for scalability, latency, and reliability

Data Engineering and Infrastructure

Collaborate with data engineers to design and optimize data pipelines for ML workloads
Implement data quality validation and monitoring systems
Work with cloud platforms (AWS, Azure, GCP) to deploy scalable ML infrastructure
Utilize big data technologies (Spark, Kafka, etc.) for large-scale data processing

Solution Architecture and Design

Design scalable and robust data science solutions that align with business requirements
Architect real-time and batch inference systems for production deployment
Implement best practices for model versioning, experiment tracking, and reproducibility
Ensure solutions follow security and compliance requirements

Leadership and Collaboration

Lead cross-functional project teams including data scientists, engineers, and business stakeholders
Mentor junior data scientists and promote knowledge sharing within the team
Collaborate with clients to understand business requirements and translate them into technical solutions
Drive innovation and adoption of new tools, techniques, and methodologies

Qualifications

Required Skills and Experience

Technical Skills

Machine Learning: Deep expertise in supervised and unsupervised learning, deep learning frameworks (TensorFlow, PyTorch), and model optimization techniques
Programming: Strong proficiency in Python and/or R, with experience in SQL and knowledge of additional languages (Java, Scala) as a plus
Data Engineering: Experience with data pipeline tools (Airflow, Prefect), big data technologies (Spark, Kafka), and data warehousing concepts
MLOps: Hands-on experience with MLOps tools (MLflow, Kubeflow, Sagemaker) and model deployment strategies (Docker, Kubernetes)
Cloud Platforms: Proficiency with cloud-based ML services (AWS SageMaker, Azure ML) and infrastructure management
Statistical Analysis: Strong foundation in statistics, experimental design, and hypothesis testing
Agentic AI: Interest or experience with agentic Artificial intelligence frameworks and multi-agent systems (LangChain, AutoGen, CrewAI, etc.) is a plus

Experience

Minimum of 5 years of experience in data science and machine learning, with at least 2 years in a senior or lead role
Proven track record of deploying machine learning models in production environments
Experience with end-to-end data science project delivery in enterprise environments
Strong understanding of software development best practices and agile methodologies

Soft Skills

Excellent communication skills, both written and verbal
Fluent in French and English (required)
Able to travel in Europe for client engagements and project delivery
Strong problem-solving abilities and analytical mindset
Ability to translate complex technical concepts for business stakeholders
Leadership experience with cross-functional teams

Preferred Qualifications

Advanced degree (Master's or PhD) in Data Science, Computer Science, Statistics, or related field
Experience with specific industry domains (finance, healthcare, retail, etc.)
Knowledge/experience with Databricks, Azure Fabric, or IBM Watson X (a plus)
Publications in peer-reviewed conferences or journals
Certifications in cloud platforms (AWS Certified Machine Learning, Azure Data Scientist Associate, etc.)

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