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Genomics is a science-led transatlantic TechBio company combining large-scale genetic and health data with proprietary analytics to accelerate drug discovery and advance predictive, preventative healthcare. The mission is to help people live longer, healthier lives through two pathways: supercharging drug discovery and development using an AI-enabled advanced genetic analytics platform, and helping people understand their personal risk of common chronic diseases through polygenic risk scores.
This Scientist role is part of the Life Sciences team and tackles major scientific challenges in harnessing genomic data to improve human health. You will work on multiple challenges across the therapeutic development cycle—from target discovery to patient stratification and finding new opportunities for existing drugs. As an early-career member of the team, you will thrive in a highly collaborative environment, developing and executing innovative and rigorous scientific approaches while effectively communicating findings to both internal and external stakeholders.
Key responsibilities include:
- Generating novel therapeutic hypotheses for diseases with unmet need by mining in-house data resources using statistical approaches to understand causal pathophysiology, identify and triage potential targets with genetic evidence, and build understanding of cell types, cellular function, tissue-level characteristics, patient populations, and biomarkers needed to support development programs.
- Applying and optimizing risk tools for patient stratification that combine genetic information (through polygenic risk scores) with conventional risk factors to identify individuals most at risk of disease onset or progression and those most likely to benefit from particular therapies.
You will bring a strong foundation in applying statistical and computational techniques in biomedical science, thrive on analyzing vast and diverse sources of 'omic data using leading statistical or AI/ML approaches to make meaningful insights into complex problems, and take pride in effectively sharing your findings with others.
The company offers hybrid working from London or Oxford offices, 25 days annual leave plus bank holidays plus a 3-day company-wide shutdown, market-leading pension scheme, comprehensive private health insurance with no excess, critical illness and life assurance, enhanced paid family leave, and a cycle-to-work scheme.
REQUIREMENTS:
- Experience using a broad array of statistical genetics approaches (e.g., GWAS, colocalization, fine-mapping, Mendelian randomization, polygenic risk scores)
- Experience defining phenotypes from electronic health records
- Knowledge of Bayesian inference, high dimensional statistics, causal inference
- Experience with software engineering practices (version control [Git & GitHub], testing, documentation, agentic coding, containerization)
- Experience building and running reproducible pipelines (e.g., using WDL, Snakemake, or NextFlow)
- Experience using cloud computing and trusted research environments (e.g., DNAnexus or Verily Workbench)
- Experience with methods development within statistical genetics
