Symbolic Resources Team Lead at Mendel

As a Symbolic Resources Team Lead, 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, drive key initiatives, and contribute to critical decision-making processes. The flexibility of remote work will allow you to manage tasks and have flexibility in joining late meetings that can be effectively handled outside the office while ensuring your in-office time maximizes your influence and leadership within the organization.

Position Overview 

Reporting to the VP of Clinical Operations, the Symbolic Resources Team Lead will lead the development of innovative clinical models and value lists. This role involves maintaining continuous training of the team by providing timely feedback, advancing Mendel's RaaS (Reasoning as a Service) systems, and supporting the implementation of LLMs initiatives. The ideal candidate should be a strategic thinker with the ability to operationalize complex clinical modeling processes while maintaining the highest standards of quality and compliance. 

The Symbolic Resources Team Lead will also be responsible for defining and executing a vision that aligns with Mendel's broader AI initiatives through robust quality frameworks. Collaboration is essential, as this role involves working closely with cross-functional teams—including clinical, AI, product, and delivery units—to address complex clinical data challenges and drive the quality and effectiveness of Mendel’s products.

Responsibilities

• Design and Strategy: Lead the design and implementation of our ontology framework for symbolic data processing, with a strong emphasis on medical databases and academic research. Ensure the accurate representation of medical knowledge within the ontologies.
• Ontology Development and Maintenance: Oversee the development and ongoing maintenance of ontologies, ensuring they are up-to-date and accurately reflect evolving medical knowledge and standards.
• Cross-functional collaboration: Collaborate closely with data scientists, healthcare professionals, and other stakeholders to understand requirements and translate raw data into actionable insights that support strategic objectives.
• Symbolic Reasoning Application: Apply advanced symbolic reasoning techniques to enhance the development of predictive models and decision-support systems, driving innovation in AI-driven healthcare solutions.
• AI Output Enhancement: Contribute to the continuous improvement of AI outputs, particularly in the analysis of medical texts, ensuring high-quality and reliable results.
• Integration of AI Techniques: Lead projects that integrate symbolic AI with machine learning models, aiming to enhance the overall data analysis capabilities and provide more robust solutions.
• Documentation and Communication: Prepare and present findings, methodologies, and best practices to both technical and non-technical audiences. Facilitate the creation of guidelines and rules for cross-functional tasks.
• Quality Assurance: Establish and manage quality frameworks for both internal and external deliverables, ensuring that customer-facing outputs meet the highest standards.
• Product and AI Optimization: Partner with internal teams to drive improvements in product and AI quality. Support the optimization of reporting tools to better meet organizational needs.
• Validation and Process Improvement: Lead efforts to validate data accuracy and the effectiveness of AI models, identifying and addressing inefficiencies in current processes.
• Continuous Learning and Innovation: Stay informed of the latest advancements in symbolic AI, knowledge representation, and their applications in healthcare. Apply this knowledge to keep the team at the forefront of industry developments.

Basic Qualifications 

• A baccalaureate degree in a relevant healthcare field, namely medicine or pharmacy, a Postgraduate degree, or research experience is nice to have.
• Deep understanding of statistics/epidemiological benchmarking and implementation of that knowledge into AI systems
• Strong familiarity with the fundamentals of oncology practice, clinical operations, and electronic health record systems.
• Experience working with HIPAA-compliant healthcare technology
• Excellent skills in essential Office 365/Google suite/writing SQL queries. applications and willingness to learn new software and tools as needed. 
• Managerial skills and people leadership are strong add-ons.
• Consistently exhibits strong critical thinking and problem-solving skills to manage complex information, assess problems, and develop practical solutions. Strong attention to detail.
• Excellent written and verbal communication and soft interpersonal skills with the ability to speak to diverse audiences
• Effectively conveys essential knowledge of cancer disease processes, diagnostics, therapeutics, and other clinical data concepts.
• Strong skills in analyzing, researching, and synthesizing large amounts of information
• Strong organizational and time management skills, with the capacity to adapt to change
• Self-starter attitude with a willingness to take initiative to drive improvements continuously. 

Preferred Qualifications

• Master's degree in bioinformatics, healthcare informatics, data science, artificial intelligence, etc.
• Specialized clinical and scientific expertise relevant to clinical oncology data, such as prior experience with real-world datasets, designing clinical algorithms or clinical phenotypes, or creating other complex logic using oncology data
• Proficiency in healthcare data and interoperability standards (e.g., HL7, FHIR, ICD-10, SNOMED, RxNorm).
• Oncology clinical research/clinical trial operations experience
• Familiarity with large language models and symbolic AI systems
• Familiarity with the basic principles, practices, and policies related to biomedical research and protection of human subjects