Senior Scientist

About the role

You will own the design and execution of high-impact statistical genetics projects that directly influence partner strategy and drug development decisions. You will act as a strategic consultant, translating complex genomic evidence into clear, actionable narratives for external pharma and biotech collaborators. This role requires you to drive innovation across the entire drug development pipeline, ensuring scientific rigor and creative problem-solving are at the forefront of every analysis. You will lead transformative initiatives by challenging conventional approaches and applying advanced statistical techniques to uncover hidden biological insights. Success in this position means you will communicate findings effectively to shape team direction and influence key decisions at critical discovery junctions. You will navigate the full spectrum of target discovery and validation, partnering closely with internal experts and external stakeholders to prioritize therapeutic opportunities. Ultimately, you will be a key architect in converting raw genetic data into robust, partner-ready strategies that accelerate predictive and preventative healthcare solutions.

Key facts

What you'll do

• Lead end-to-end analysis of large-scale genomic datasets, transforming raw genetic information into strategic insights for partner projects.
• Design and implement advanced statistical models to dissect complex disease mechanisms and identify high-value therapeutic targets.
• Partner with external collaborators to co-develop innovative analysis strategies that push the boundaries of current genetic epidemiology.
• Mine and manage expansive genomic and health data repositories to extract robust, reproducible signals underpinning drug discovery programs.
• Translate intricate genetic associations into clear, visual, and conceptual narratives that guide clinical strategy and patient stratification efforts.
• Serve as a statistical domain expert, advising on best practices for human genetics applications within fast-paced, collaborative projects.
• Coordinate with cross-functional internal teams to integrate genomic evidence into comprehensive drug development roadmaps.
• Deliver high-impact presentations and consultative outputs that influence key decisions and shape the direction of partner engagements.
• Champion the application of polygenic risk scores and target validation methodologies to refine patient selection and trial design.
• Explore novel algorithmic approaches to enhance data quality, reduce technical noise, and improve the interpretability of large-scale studies.
• Lead the interpretation of genome-wide association studies, ensuring findings are rigorously validated and biologically plausible.
• Drive the creation of predictive frameworks that link genetic architecture to treatment response and disease progression outcomes.

Requirements

• Hold a PhD or equivalent research experience in genetics, bioinformatics, computational biology, or a closely related quantitative field.
• Demonstrated experience performing foundational genetic association analyses, including genome-wide association studies (GWAS) and polygenic risk scores (PRS).
• Show strong competency in statistical programming, utilizing languages such as R and Python to conduct large-scale genomic data analysis.
• Provide evidence of data mining and management experience with large, complex genomic datasets in high-performance computing environments.
• Exhibit a robust understanding of human population genetics, including principles of linkage disequilibrium, allele frequency, and ancestry inference.
• Display confident engagement with diverse stakeholders, including the ability to present complex scientific concepts to non-specialist audiences.
• Proven ability to deliver presentations and contribute to client-facing materials that articulate the business impact of genomic insights.
• Commitment to maintaining the highest standards of data integrity, reproducibility, and scientific ethics in all analytical processes.
• Ability to thrive in a hybrid work model, splitting time between our London office and collaborative field activities as required.
• Willingness to adhere to our strict no-recruitment-agency policy, ensuring all applications are direct and compliant with our hiring principles.

Nice to have

• Experience with advanced machine learning techniques applied to genomic prediction.
• Familiarity with regulatory standards and guidelines relevant to clinical Genomics and diagnostics.

Practical notes

Location: Hybrid roles are based in London or Oxford.

Engagement: This is a full-time position.

Recruitment agencies are not permitted to contact us regarding this role, and we do not accept unsolicited CVs from third parties.