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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 company operates in two main areas: supercharging drug discovery and development through 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 focuses on tackling major scientific challenges in harnessing genomic data to improve human health. You will work across the therapeutic development cycle, including discovering and validating therapeutic targets and developing patient stratification strategies. 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 disease pathophysiology, identifying and triaging potential targets with genetic evidence, and building 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 extract meaningful insights into complex problems, and take pride in effectively sharing your findings with others.
REQUIREMENTS:
Must have:
- Solid foundations in statistics (modeling, regression)
- Solid foundations in genetics and molecular biology
- Statistical programming experience (e.g., Python or R)
Preferred:
- Experience with statistical genetics approaches (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 (WDL, Snakemake, NextFlow)
- Experience with cloud computing and trusted research environments (DNAnexus, Verily Workbench)
- Experience with methods development within statistical genetics
