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.

Requirements

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.

Benefits

health care and other insurance benefits (for employee and family)
retirement benefits
paid holidays
vacation
paid caregiver/parental and medical leave

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

computational biologybioinformaticsmachine learningcomputational pathologystatistical methodsanalytical methodshigh-dimensional data analysisbiomarker analysisAI/ML analytical approachesadvanced imaging techniques

Soft Skills

leadershipinfluencecommunicationcollaborationaccountabilitycross-functional teamworkstrategic thinkingproblem-solvingtimely communicationstakeholder engagement

Certifications

PhD degree