Clinical Data Scientist (Clinical Informatics Team) - QuantHealth

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.

As a Clinical Data Scientist in the Clinical Informatics Team (R&D), you will play a key role in building production-grade AI-powered data systems and intelligent clinical data workflows that transform complex real-world clinical data into structured, production-ready assets for QuantHealth’s clinical simulation platform. This role sits at the intersection of data engineering, applied AI, and biomedical data systems. You will work closely with clinical data scientists, Clinical Informaticians, DataOps, and engineering teams to develop and productionize intelligent workflows for large-scale clinical data processing, information extraction, data harmonization, and AI-assisted clinical data curation. The role focuses heavily on working with large-scale structured and unstructured healthcare data, including clinical notes, longitudinal patient records, biomedical literature, and real-world evidence datasets. You will design and maintain production-grade pipelines and AI workflows that balance extraction quality, scalability, operational reliability, and computational cost. The ideal candidate combines strong data science and data engineering capabilities with hands-on experience developing AI-powered data workflows using modern Python-based tooling and LLM technologies. Healthcare or biomedical data experience is a strong advantage, but we also welcome technically strong candidates with demonstrated ability to learn complex domains quickly. You will own AI-powered clinical data workflows end-to-end, from early prototyping through production deployment, monitoring, and iterative improvement.

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.