Clinical Informatician

As a physician joining our Informatics Ontology Team, your clinical perspective will be essential in guiding the design and validation of AI-driven solutions for revenue cycle management (RCM) and IQ guideline–based workflows. You will work in a hybrid capacity, with a balance of remote work and in-office presence. Your role is strategically impactful, requiring you to be physically present in the office on designated days to collaborate closely with cross-functional teams. The flexibility of remote work will allow you to join late meetings/discussions that can be effectively attended outside the office while ensuring your in-office time maximizes your communication with your team and the team in the US office.

RESPONSIBILITIES

• Design and Strategy:
  Design and implement our ontology framework for symbolic data processing, with a strong emphasis on medical databases, academic research and clinical standards. Ensure the accurate representation of medical knowledge within the ontologies.
• Ontology Development and Enrichment:
  Develop, maintain, and enrich ontologies to reflect evolving medical knowledge, guidelines, and best practices. Incorporate feedback from clinical and informatics teams and correctly classify and review their suggestions to continuously expand and refine the ontology.
• Ontology Release Management:
  Oversee the ontology release process, including edits implementation, versioning, changelog documentation and quality assessment for each release. Ensure transparency, traceability, and reliability of updates.
• Ontology Tools Management:
  Manage and utilize ontology tools and resources, including efficient use of protege and internal editing platforms (e.g., Streamlit apps). Support the development and optimization of these tools to enhance team efficiency and ontology quality.
• AI Output Enhancement:
  Contribute to the continuous improvement of AI outputs, particularly in the analysis of medical texts, ensuring high-quality and reliable results.
• Documentation and Communication:
  Document and present findings, methodologies, and best practices to both technical and non-technical audiences. Share in the creation of cross-functional tasks' guidelines and rules.
• Product and AI Optimization:
  Partner with internal teams to drive improvements in product and AI quality. Assist in projects integrating symbolic AI with machine learning models aiming to enhance the overall data analysis capabilities and provide more robust solutions.
• Validation and Process Improvement:
  Validate each release component (ontology, Symbolic resources etc.) identifying and solving inefficiencies in current processes and propose process improvements.
• Continuous Learning and Innovation:
  Stay current with advancements in symbolic AI, ontology engineering, knowledge representation, and their applications in healthcare and RCM.

BASIC QUALIFICATIONS

• Doctor of Medicine (MD) or equivalent baccalaureate degree in medicine with minimum grade "Very Good".
• Proficient use of Google and Microsoft Suite applications.
• Specialized clinical and scientific expertise relevant to clinical data, such as prior experience with real-world datasets, designing clinical algorithms or clinical phenotypes, or creating other complex logic using clinical data.
• Familiarity with healthcare data and interoperability standard terminologies. (e.g., HL7, FHIR, ICD-10, SNOMED CT, RxNorm).
• Strong understanding of disease processes, diagnostics, therapeutics, and clinical workflows, with the ability to translate this knowledge into structured concepts.
• Strong skills in analyzing, researching, and synthesizing large amounts of data.
• Strong organizational and time management skills, with the capacity to adapt to change.

PREFERRED QUALIFICATIONS

• Understanding and familiarity with the basics of AI, such as, but not limited to, Machine learning, Symbolic AI, LLMs, and Formal Logic.
• Knowledge of programming or scripting languages (e.g., Python, SQL, R) is nice to have.
• Prior experience with ontology development, ontology enrichment, or clinical knowledge representation.
• Hands-on experience with data cleaning, manipulation, or validation in healthcare contexts.
• Ability to design, conduct, and interpret analyses to support clinical decision-making and healthcare outcomes.
• Clinical research experience, particularly involving structured data capture or analysis.
• Familiarity with the basic principles, practices, and policies related to biomedical research and protection of human subjects.
• A postgraduate degree or equivalent research experience in informatics, bioinformatics, or a related field is desirable.