Director, Emerging Diagnostic Technologies

Role Summary

Director of Emerging Diagnostic Technologies leads innovation in diagnostic technology platforms within Oncology Translational Medicine Integrative Sciences, focusing on leveraging oncology foundational models, real-world data, multi-modal omics, AI/ML, computational pathology, and biomarker discovery to transform cancer understanding and treatment. Shapes biomarker, diagnostic, and clinical development strategies, particularly for ADCs, T cell engagers, and other advanced modalities, while analyzing clinical trial biomarker data to enable translational medicine approaches.

Responsibilities

• Lead innovation in emerging diagnostic technologies, including ctDNA, computational pathology, advanced imaging techniques, and multi-modal biomarker platforms, to enhance patient stratification, therapeutic targeting, and mechanism-of-action insights.
• Develop and apply oncology foundational models and AI/ML analytical approaches to complex clinical, real-world and biomarker datasets, including high-dimensional data such as single-cell transcriptomics, spatial omics, proteomics, and cfDNA.
• Serve as a subject matter expert in application of analytical methods and emerging diagnostic technologies to enable biomarker discovery and diagnostic strategies to optimize patient selection, dose determination, and combination therapy approaches with a focus on antibody-drug conjugates (ADCs).
• Oversee the integration of oncology foundational models to inform translational medicine and clinical development strategies ensuring robust interpretation, practical implementation and timely communication across matrix teams.
• Collaborate with cross-functional teams, clinical development, CPMS (Clinical Pharmacology and Modeling Simulation), Diagnostic, Translational Research, and AI/ML teams, to evaluate and apply complementary data analytics approaches for meaningful insights into complex biology.
• Lead efforts to identify, evaluate, and implement emerging technology platforms to advance diagnostic innovation and support pipeline growth.
• Provide leadership and accountability for clear and timely communication of data analysis outputs, complex analytical principles, and models to diverse stakeholders, including senior leaders and non-technical partners.
• Champion the integration of biomarker and diagnostic strategies into clinical development plans, ensuring alignment with oncology research unit and translational medicine objectives
• Drive the application of computational pathology and AI-driven image analysis to enhance diagnostic capabilities and biomarker discovery in oncology programs.
• Support the evaluation of academic partnerships and external technology platforms, ensuring alignment with Oncology Translational Medicine goals and innovation priorities.
• Contribute to due diligence efforts for business development opportunities, leveraging expertise in advanced analytics and emerging diagnostic technologies.

Qualifications

• PhD degree or equivalent experience/training in computational biology, bioinformatics, machine learning, computational pathology, or a related field.
• 7+ years of applied experience in Pharma/Biotech or an academic setting, with a focus on oncology research, diagnostic innovation, and biomarker analysis.
• Demonstrated ability to lead and influence cross-functional teams in a matrix environment, driving alignment and delivering impactful outcomes.
• Experience with emerging diagnostic platforms and technologies, including their application to translational medicine and clinical development.
• Demonstrated advanced knowledge of statistical and analytical methods relevant to the analysis of complex high-dimensional heterogeneous datasets.
• Experience with GitHub, development of R Shiny applications/R markdown, and working in cloud or high-performance computing (HPC) environments.

Preferred Qualifications

• MD degree with oncology clinical development experience is highly desirable and considered an upside for this role.
• Expertise in oncology foundational models and AI/ML analytical approaches applied to complex biomarker datasets.
• Proficiency in coding skills (e.g., R, Python) and strong working knowledge of bioinformatics databases, resources, and tools.
• Proven ability to analyze and interpret high-dimensional datasets (e.g., single-cell and spatial transcriptomics, proteomics, cfDNA) using advanced modeling techniques.
• Experience with computational pathology and AI-driven image analysis in the context of diagnostic innovation.
• Strong knowledge of clinical trial biomarker data analysis and its application to precision medicine strategies.
• Demonstrated experience with antibody-drug conjugates (ADCs) and their associated biomarker and diagnostic strategies.
• Proven leadership experience in driving diagnostic innovation and implementing emerging technologies in oncology research.
• Strong track record of integrating preclinical and clinical biomarker data to inform translational medicine strategies.
• Familiarity with regulatory requirements and data standards for transitioning programs into clinical trials.
• Strategic mindset with the ability to influence and drive decision-making in a matrix environment.