Senior Data Scientist

Eno Health is an AI-powered platform designed to be the ultimate decision-support system for healthcare providers seeking to facilitate personalised care and long-term wellness for patients. We are building a compliant, secure, and European-sovereign biomedical AI solution for personalised healthcare. The platform streamlines practitioners’ workflows and patient data processing, enabling faster, more precise clinical decisions.

Role Description

You will own the data-science layer of a regulated clinical AI system: the models, the evaluation methodology, and the statistical rigour behind the knowledge graph. This is not a dashboards-and-churn-models role. The problems are causal reasoning over structured clinical knowledge, mapping messy real-world lab data onto biomedical ontologies, and evaluating a fine-tuned biomedical LLM to a standard that survives a medical-device audit.

You will work with clinical, engineering and product colleagues to turn clinical knowledge into computable, testable artefacts. Expect a mix of modelling, ontology work, evaluation design, and writing, as every model decision at ENO needs to be documented, reviewed by a named human, and reproducible. Clinical sign-off gates what enters the knowledge graph; your job is to provide clinicians the statistical evidence to sign off.

This is a full-time hybrid role based in the Brussels Metropolitan Area, with some flexibility for remote work.

• Advanced Python and PyTorch; experience taking models from experiment to production. Our inference stack is self-hosted (vLLM); familiarity with parameter-efficient fine-tuning (LoRA/QLoRA) of open medical or biomedical LLMs is a strong advantage.
• Grounding in probabilistic graphical models; Bayesian networks, causal inference (structural causal models, do-calculus, or counterfactual reasoning). Our core asset is a causal clinical knowledge graph; this is the reasoning substrate you will work on daily.
• Experience with property graph databases (Neo4j preferred): graph data modelling, graph algorithms (PageRank, random walks), and retrieval over structured knowledge (graph-RAG architectures).
• Working knowledge of biomedical ontologies and terminologies (eg. SNOMED CT, LOINC/UCUM, Mondo, HPO, or equivalents) and the practical realities of mapping messy clinical and lab data onto them.
• Applied statistics for model evaluation: experimental design, hypothesis testing, calibration, and error analysis and the discipline to document it. Model validation at ENO feeds a medical-device technical file (EU MDR, IEC 62304); reproducibility and named-reviewer sign-off are requirements of the job, not aspirations.
• Experience with large, sensitive datasets in healthcare or a similarly regulated domain; fluency in GDPR Article 9 constraints, pseudonymisation, and data-minimisation trade-offs.
• Master’s or PhD in a quantitative field, or equivalent practical experience.
• Federated or distributed learning: training and evaluating models across data silos that cannot be centralised. Our sovereign architecture keeps patient data inside per-country cells; learning across cells without moving personal health data is where this platform is heading.
• Privacy-enhancing technologies beyond access control: differential privacy, secure aggregation, and conceptual command of homomorphic encryption (enough to reason about what is feasible, at what cost, and when it is the wrong tool).
• Streaming data experience (Kafka/Flink) for clinical, lab, and wearable ingestion pipelines.
• FHIR R4 and clinical interoperability standards.
• Understanding of functional or systems medicine, or P4 (predictive, preventive, personalised, participatory) medicine frameworks.

• A foundational role in a clinical AI platform at the stage where architecture decisions are still being made and yours to influence.
• Competitive salary and an equity package.
• Hybrid working from our Brussels base, with flexibility for remote work.
• Direct collaboration with the founding team and clinical leadership.