Clinical Data Scientist (Clinical Informatics Team)
On-site · Tel Aviv, Tel Aviv, Israel
Job Summary
Clinical Data Scientist in the Clinical Informatics Team to design, develop, and maintain scalable AI/data pipelines for processing large-scale structured and unstructured clinical data. Build production-grade LLM-based systems for clinical note extraction, phenotype extraction, entity normalization, literature mining, and biomedical information retrieval; develop automated clinical data curation and structured extraction workflows; harmonize heterogeneous datasets; design longitudinal representations and feature engineering; implement human-in-the-loop validation; and produce robust, scalable AI workflows and data systems using Python, Spark, Databricks, SQL, and cloud environments. Roles involve collaboration with CDS researchers, DataOps, and engineering to productionize AI workflows for QuantHealth’s clinical simulation platform; focus on quality, reliability, and cost efficiency of healthcare data pipelines and AI-powered data workflows.
Required Qualifications
- MSc or BSc in computer science, data science, computational biology, biomedical engineering, or a related quantitative field
- 3+ years of hands-on experience in applied AI, data engineering, data science, machine learning engineering, or large-scale data systems development
- Strong Python programming skills and experience building modular, production-quality data pipelines
- Practical experience with SQL, Spark, Databricks, and large-scale distributed data processing environments
- Experience building and maintaining production-grade AI systems, LLM workflows, or large-scale data pipelines, including testing, validation, monitoring, and performance optimization
- Experience working with LLM-based systems, structured extraction pipelines, retrieval workflows, or AI-assisted automation systems
- Strong understanding of software engineering best practices, including code organization, testing, reproducibility, and maintainability
- Experience working with APIs, cloud-based environments, and scalable data infrastructure
- Strong problem-solving abilities and ability to work effectively in highly ambiguous and evolving environments
- Excellent communication and collaboration skills in cross-functional technical organizations
- Experience with healthcare, biomedical, clinical, or real-world patient data (preferred)
- Experience developing healthcare NLP or clinical note extraction systems (preferred)
- Experience with agentic AI systems, retrieval-augmented generation (RAG), or structured output pipelines (preferred)
- Experience building internal AI applications, QA tooling, dashboards, or lightweight full-stack data products (preferred)
- Experience with PyTorch, MLflow, FastAPI, Streamlit, Snowflake, or modern AI engineering frameworks (preferred)
- Familiarity with healthcare ontologies and vocabularies such as ICD, SNOMED, RxNorm, LOINC, or OMOP (preferred)
- Experience working with messy, heterogeneous, longitudinal real-world data (RWD/EHR) (preferred)
- Experience with temporal modeling, missing data handling, imputation methodologies, or longitudinal patient-level feature engineering (preferred)
- Experience in healthcare analytics, HMOs, biotech, pharma, computational biology, or biomedical AI environments (preferred)
- Experience balancing AI workflow quality, scalability, latency, and operational cost in production environments (preferred)
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