Clinical Informatics Scientist at QuantHealth in Israel

About QuantHealth QuantHealth is a growing AI startup in the clinical trial space, leveraging AI, biomedical data, knowledge graphs, and real-world patient data to simulate and optimize clinical trials for pharmaceutical companies. Our platform helps customers simulate clinical trials, reduce development risk and cost, shorten timelines, and improve the probability of clinical trial success. About the Role As a Clinical Informatics Scientist in the Clinical Informatics team (R&D), you will play a central role in translating complex clinical and biomedical concepts into robust, computable data representations that power QuantHealth’s clinical simulation platform. This role sits at the intersection of clinical science, real-world data (RWD), and applied analytics. You will work closely with clinical data scientists, delivery teams, and data engineers to construct clinically valid indication definitions, engineer disease- and treatment-specific features, and develop scalable cohort logic, phenotype libraries, and reusable indication frameworks that power QuantHealth’s simulation and retrospective analysis platform. The ideal candidate combines strong clinical understanding with hands-on technical and analytical capabilities. You should be comfortable working with large clinical datasets, developing analytical workflows, and writing analytical code, validating clinical assumptions against real-world data, and translating clinical protocols into structured computational logic. This position is especially well suited for clinically trained professionals with experience working with EHR/RWD datasets and strong interest in data-driven clinical research, clinical informatics, and AI-enabled healthcare systems. The role requires strong clinical judgment and the ability to operate effectively in ambiguous and imperfect real-world data environments. As the Clinical Informatics organization grows, team members will have opportunities to deepen therapeutic area expertise and evolve into domain-focused scientific leadership roles, including therapeutic area lead and principal-level positions. Responsibilities Develop clinically valid indication and cohort definitions using real-world clinical data sources, including EHR, claims, registry, and clinical trial datasets. Translate clinical trial protocols, eligibility criteria, endpoints, and study designs into computable cohort logic and structured analytical definitions. Engineer and validate disease-specific and treatment-specific clinical features, surrogate endpoints, and progression markers across multiple therapeutic areas. Construct and validate longitudinal clinical concepts such as oncology lines of therapy, disease progression endpoints, treatment sequencing, and retrospective outcome measures. Design and maintain reusable indication frameworks, phenotype definitions, and disease-specific clinical logic that support scalable clinical simulation and retrospective analysis workflows. Develop standardized clinical feature libraries, progression definitions, and phenotype logic across therapeutic areas. Develop and maintain analytical workflows and datasets supporting QuantHealth’s simulation and retrospective analysis pipelines. Query, analyze, and validate large-scale clinical datasets using Python, SQL, and modern analytical tooling. Collaborate closely with clinical data scientists, delivery teams, and data engineers to ensure clinical consistency, reproducibility, and scalability of data products. Validate clinical assumptions and engineered features against literature, clinical guidelines, and real-world evidence. Support quality assurance and clinical validation processes for internal and customer-facing analyses. Contribute to the evolution of QuantHealth’s internal clinical knowledge infrastructure and reusable clinical informatics assets. Leverage modern AI and LLM-based tools to improve clinical data curation, protocol interpretation, and knowledge extraction workflows. Qualifications MD, PhD, PharmD, MSc, MPH, RN, or equivalent degree in clinical informatics, epidemiology, life sciences, biomedical sciences, pharmacy, nursing, public health, or a related clinical or quantitative discipline. 2+ years of hands-on experience working with real-world healthcare data, EHR data, clinical analytics, clinical informatics, or data-driven healthcare research. Practical experience working with clinical datasets and translating clinical concepts into structured analytical definitions. Hands-on experience using Python and SQL for clinical data analysis and data manipulation. Strong understanding of clinical trial design, inclusion/exclusion criteria, endpoints, and longitudinal patient data. Experience developing or validating cohort definitions, phenotypes, progression endpoints, or retrospective clinical features. Familiarity with statistical concepts, observational analyses, and clinical data validation methodologies. Ability to independently investigate clinical questions using both data and scientific literature. Strong analytical thinking, attention to detail, and scientific rigor. Excellent collaboration and communication skills in cross-functional clinical and technical environments. Strong Advantages Experience working with oncology, immunology & inflammation (I&I), cardiometabolic, or neurology datasets and clinical concepts. Experience working with large-scale RWD/EHR environments and distributed data systems. Experience with ETL pipelines, clinical data modeling, or healthcare ontologies and vocabularies. Familiarity with Spark, dashboarding tools, or cloud-based analytics environments. Experience leveraging LLMs or AI-assisted workflows for clinical data interpretation or curation. Understanding of healthcare coding systems and clinical vocabularies such as ICD, SNOMED, RxNorm, LOINC, or OMOP. Experience in pharma, biotech, healthcare analytics, hospital informatics, HMOs, CROs, or clinical research organizations.