Data Scientist: ML, NLP & AI Pipeline Innovator

Phoenix Technologies; Brussel; Sur place; EUR 60 000 - 90 000; Plein temps

Résumé du poste

Phoenix Technologies seeks a Data Scientist to design advanced analytics solutions and build ML/NLP models that drive data-driven business impact.

You will develop reproducible ML pipelines, engineer training data via ETL, and architect scalable storage for AI workloads. Collaboration across teams and strong stakeholder communication are essential for translating complex problems into actionable insights.

Qualifications

• 3+ years of experience in data science or analytics.
• Strong knowledge of advanced analytics, ML, and NLP.
• Proficiency in programming languages used for modeling (Python, R, etc.).
• Experience with MLOps practices and model reproducibility.
• Familiarity with ETL, data engineering, and scalable data storage technologies.
• Excellent presentation and stakeholder communication skills.

Responsabilités

• Design and implement data-driven solutions using advanced analytics techniques.
• Build predictive models and text-based models leveraging ML and NLP.
• Develop and maintain reproducible ML pipelines following MLOps practices.
• Prepare and engineer training datasets via ETL processes and data engineering.
• Architect scalable data storage solutions for AI workloads.
• Validate models through experiment design and hypothesis testing.
• Visualize and communicate model outputs using BI tools.
• Collaborate with cross-functional teams to translate business problems into DS solutions.

Connaissances

Data science

Machine learning

NLP

Python

R

MLOps

ETL

Data engineering

Stakeholder communication

Outils

Python

R

TensorFlow/PyTorch

SQL

Description du poste

Phoenix Technologies seeks a Data Scientist to design advanced analytics solutions and build ML/NLP models that drive data-driven business impact.

You will develop reproducible ML pipelines, engineer training data via ETL, and architect scalable storage for AI workloads. Collaboration across teams and strong stakeholder communication are essential for translating complex problems into actionable insights.