We are seeking a visionary leader to join our Oncology Translational Medicine Integrative Sciences team as the Director of Emerging Diagnostic Technologies. This role will focus on driving innovation in diagnostic technology platforms, leveraging cutting-edge approaches such as oncology foundational models, real world data, multi-modal omics data, AI/ML, computational pathology, and biomarker discovery to transform how we understand and treat cancer. The successful candidate will play a pivotal role in shaping biomarker, diagnostic, and clinical development strategies, particularly in the context of antibody-drug conjugates (ADCs), T cell engagers and other advanced therapeutic modalities, while maintaining responsibilities for analyzing clinical trial biomarker data to enable translational medicine approaches.
Key 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.
Basic Qualifications:
We are looking for professionals with these required skills to achieve our goals:
- 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.
We are seeking a visionary leader to join our Oncology Translational Medicine Integrative Sciences team as the Director of Emerging Diagnostic Technologies. This role will focus on driving innovation in diagnostic technology platforms, leveraging cutting-edge approaches such as oncology foundational models, real world data, multi-modal omics data, AI/ML, computational pathology, and biomarker discovery to transform how we understand and treat cancer. The successful candidate will play a pivotal role in shaping biomarker, diagnostic, and clinical development strategies, particularly in the context of antibody-drug conjugates (ADCs), T cell engagers and other advanced therapeutic modalities, while maintaining responsibilities for analyzing clinical trial biomarker data to enable translational medicine approaches.
Key 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.
Basic Qualifications:
We are looking for professionals with these required skills to achieve our goals:
- 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.
Please visit GSK US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees.
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