Senior Scientist Genomics

Overview

In this role, you apply human genetics and genomics to identify patient populations and inform clinical and commercial strategies. You will work across Clinical, Commercial, Real World Evidence, and Research teams to translate genomic findings into actionable recommendations. You will build and manage external collaborations with academic groups, CROs, and data providers to support patient identification and epidemiological analyses. Your work will combine AI/ML and advanced statistics to interpret large datasets and guide portfolio decisions. This is a cross-functional, data-driven role with meaningful impact on therapies for rare genetic diseases.

Responsibilities

• Integrate human genetics, literature, EHR/claims, and genetic-testing data to define disease segments, biomarkers, and patient populations
• Collaborate with Clinical, Commercial, Real World Evidence, Research, and Business Development teams to translate genomic data into clinical and commercial actions
• Establish and manage external collaborations with academic investigators, CROs, testing labs, and data providers, including defining scope and timelines
• Evaluate external cohorts and data providers for patient-identification, prevalence, and genotype-phenotype analyses
• Apply statistical genetics, epidemiology, AI/ML, and advanced methods to large-scale genomic and real-world datasets with reproducibility and rigor
• Contribute to portfolio prioritization and business development due diligence via genetic rationale and data gaps
• Analyze whole-exome and whole-genome sequence data combined with phenotypic/genomic data
• Communicate recommendations clearly to both scientific and non-scientific stakeholders

Key requirements

• PhD or equivalent experience in human genetics, statistical genetics, bioinformatics, computational biology, or genetic epidemiology
• At least 3 years of biotech/pharma experience with cross-functional collaboration
• Experience analyzing large-scale human genetic/genomic datasets (WES, WGS, array, EHR-linked, etc.)
• Familiarity with genomic or patient-identification resources (UK Biobank, All of Us, etc.)
• Experience programming in R and/or Python; exposure to cloud computing and scalable data environments
• Hands-on experience applying AI/ML or advanced statistics to large biomedical datasets
• Experience initiating or managing external collaborations with academic groups, CROs, or data providers
• Ability to synthesize multiple evidence types and make actionable recommendations when data is incomplete
• Strong written and verbal communication skills suited to diverse stakeholders

• strong written and verbal communication
• cross-functional collaboration
• pragmatism
• statistical genetics
• epidemiology
• AI/ML

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