Design, execute, and interpret biomarker and translational analyses to support clinical development programs, including target engagement & stratification, pharmacodynamic modeling, patient stratification, mechanism of action validation, indication selection, and benefit–risk assessments.
Develop and apply robust analytical workflows for high-content, multi-modal clinical data, including bulk and single-cell genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial omics, imaging, and emerging assay modalities.
Translate complex biological and clinical questions into quantitative analysis plans, statistical models, and computational frameworks that generate actionable insights.
Integrate internal clinical trial data with external datasets (e.g., public omics resources, real-world data, literature-derived knowledge) to contextualize findings and inform program strategy.
Contribute to portfolio-level analyses and cross-asset learnings through principled data mining, visualization, and knowledge discovery approaches.
Partner closely with biologists, clinicians, assay scientists, and data engineering teams to ensure analytical rigor, data quality, and scientific relevance.
Clearly communicate analytical approaches, assumptions, limitations, and conclusions to diverse audiences through written reports, presentations, and cross-functional forums.
Operate effectively in a global, matrixed environment, including regular collaboration across time zones with US- and EU-based teams.
Strategically leverage AI to enhance speed, accuracy and insightfulness of results, maximally integrating relevant findings in the public domain.
Familiarity with clinical biomarker platforms and data types, such as NGS, flow cytometry, IHC, immunoassays, imaging, and transcriptional profiling.
Demonstrated experience analyzing complex, large-scale biological.