Senior Clinical AI Data Scientist - Causal Graphs

Eno Health; Brussel Hoofdstad; Hybride; EUR 90 000 - 130 000; Plein temps

Avantages offerts par ce poste

Competitive salary

Equity package

Hybrid work from Brussels

Direct collaboration with founders

Résumé du poste

Eno Health is building an AI-powered clinical decision-support platform and is seeking a data-science lead to own the models, evaluation plans, and statistical rigour behind the knowledge graph.

You will work with clinical, engineering and product teams to turn clinical knowledge into computable artefacts, with strict documentation, review, and reproducibility requirements. The role is full-time hybrid based in Brussels with some remote flexibility.

Qualifications

• Advanced Python and PyTorch; production-ready models.
• Strong grounding in probabilistic graphical models and causal inference.
• Experience with property graphs and Neo4j.
• Practical mapping of clinical data to ontologies (SNOMED, LOINC, etc.).
• Experiments, statistics, and model validation for medical-device standards.
• Familiarity with GDPR constraints and data minimisation.

Responsabilités

• Own the data-science layer of a regulated clinical AI system.
• Develop models, evaluation methods, and verification protocols.
• Publish evidence for clinical sign-off and audit readiness.
• Collaborate with clinical, engineering and product teams.

Connaissances

Python

PyTorch

Bayesian networks

Causal inference

Graph databases

Neo4j

Ontology mapping

GTMS/graph‑RAG

GDPR compliance

Federated learning

Formation

Master's or PhD in a quantitative field

Outils

vLLM

LoRA/QLoRA

Kafka/Flink

Mondo/HPO/SNOMED/LOINC

Description du poste

Eno Health is building an AI-powered clinical decision-support platform and is seeking a data-science lead to own the models, evaluation plans, and statistical rigour behind the knowledge graph.

You will work with clinical, engineering and product teams to turn clinical knowledge into computable artefacts, with strict documentation, review, and reproducibility requirements. The role is full-time hybrid based in Brussels with some remote flexibility.