Responsibilities

• Carry out analyses that integrate human genetics, literature, EHR/claims data, genetic-testing data, and other evidence to define disease segments, biomarkers, diagnostic pathways, and patient populations.
• Partner closely with Clinical, Commercial, Real World Evidence, Research, Business Development, and other teams to translate genomic evidence into actionable clinical and commercial recommendations.
• Establish and manage external collaborations with academic investigators, CROs, testing laboratories, and data providers, including defining analytical scope, timelines, deliverables, data needs, and decision criteria.
• Evaluate external cohorts, genetic-testing laboratories, CROs, and data providers for patient-identification, prevalence, and genotype-phenotype analyses.
• Apply statistical genetics, epidemiology, AI/ML, and advanced statistical methods to large-scale genomic and real-world datasets; ensure analyses are reproducible, appropriately rigorous, and decision-oriented.
• Contribute to portfolio prioritization and business development diligence as needed by assessing genetic rationale, patient-identification feasibility, population size, and data gaps.
• Apply and develop statistical and computational approaches to analyze and interpret whole-exome and whole-genome sequence data in combination with phenotypic or other genomic data.
• Communicate recommendations to scientific and non-scientific stakeholders, clearly distinguishing evidence, assumptions, uncertainty, tradeoffs, and next steps.

Required qualifications

• PhD or equivalent experience in human genetics, statistical genetics, bioinformatics, computational biology, or genetic epidemiology.
• At least 3 years of biotechnology or pharmaceutical industry experience with demonstrated cross-functional work across research and non-research functions, such as Clinical, Commercial, Real World Evidence, Medical Affairs, Patient Identification, or Business Development.
• Expertise analyzing large-scale human genetic or genomic datasets, such as whole-exome, whole-genome, array, genotype-phenotype, EHR-linked, claims-linked, registry, or clinico-genomic datasets.
• Familiarity with genomic, real-world, or patient-identification resources such as UK Biobank, All of Us, genetic-testing laboratory datasets, EHR/claims networks, registries, or comparable sources.
• Experience programming in R and/or Python; experience with cloud computing, workflow systems, or scalable data environments is strongly valued. Hands-on experience applying AI/ML or advanced statistical methods to large biomedical datasets, beyond general-purpose chatbot use.
• Experience initiating or managing external collaborations with academic groups, CROs, data providers, genetic-testing laboratories, or similar partners.
• Ability to synthesize multiple evidence types, make assumptions explicit, and recommend a practical course of action when data are incomplete, imprecise, or time-constrained.
• Strong written and verbal communication skills; comfortable spending a substantial portion of time with clinical, commercial, and other non-research stakeholders.

Preferred

• Experience with patient finding, prevalence estimation, rare-disease epidemiology, genetic screening, clinical-trial feasibility, or commercial patient-identification strategy.

Note: This description is not intended to be all-inclusive, or a limitation of the duties of the position. It is intended to describe the general nature of the job that may include other duties as assumed or assigned.

Equal Opportunity Employer/Veterans/Disabled

An Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.