Associate Director - Precision Medicine Data & Analytics

Role Summary

Associate Director, Precision Medicine Data and Analytics responsible for developing clinical biomarker analysis plans, conducting data analysis to summarize and interpret clinical trial data, and integrating development objectives with global considerations into strategic decisions and advanced data analytics for assigned portfolio programs.

Responsibilities

• Oversee the analysis of complex biological data from clinical trials, ensuring robust interpretation and reporting for clinical study reports and regulatory submissions
• Support the delivery of clinical biomarker data analysis to enable Oncology Translational Medicine strategies and decision making towards identification of the right patient, right drug, right dose at the right time
• Integrate and analyze large-scale, high-dimensional, and multimodal biomarker datasets from internal and external sources to enhance understanding of mechanisms of action, resistance, patient stratification and selection, new indications, and biologically driven combination strategies
• Collaborate with CPMS, Diagnostic, Translational Research and AI/ML teams to ensure complementary data analytics approaches are evaluated and applied to deliver meaningful insights
• Clear and timely communication of data analysis outputs and complex analytical principles & models to matrix team partners
• Maintain data integrity principles aligned with human data quality standards
• Support integration and analysis of academic biomarker partnership data and technology evaluation data generated by the Oncology Translational Medicine team
• Execute internal and external biomarker data analysis to support pipeline growth, including life cycle management plans and leveraging data analytics outputs to support decision making
• Execute advanced analytics and target/pathway analysis to support evaluation of due diligence business development asset evaluations

Qualifications

• PhD degree or equivalent computational biology, bioinformatics, machine learning experience/training
• 5+ years of experience in Pharma/Biotech or academic setting
• Experience with coding skills in R and/or Python and working knowledge of common bioinformatics databases, resources and tools
• Experience dealing with next-generation sequencing data and working with oncology drugs or programs
• Experience communicating analytical principles and results to non-analytical colleagues and stakeholders (Preferred)
• Experience working with and analyzing complex high-dimensional datasets (e.g., single-cell and spatial transcriptomics, proteomics, cfDNA)
• Experience with GCP principles and working on clinical studies
• Experience with data and metadata best practices (e.g., FAIR principles, data standards, cloud environment analytical tools)
• Experience with data visualization outputs and multi-disciplinary teams
• Experience with statistical methods relevant to analysis of complex high-dimensional heterogeneous datasets
• Experience with GitHub, development of R Shiny applications/R markdown, and cloud or HPC environments (plus)

Education

• PhD in computational biology, bioinformatics, or related field (or equivalent)

Additional Requirements