Summary

At Genomics, we tackle major scientific challenges in harnessing genomic data to improve human health. This role is part of the Life Sciences team, working on a range of challenges spanning the therapeutic development cycle – including discovering and validating therapeutic targets and developing patient stratification strategies. The Scientist is an early-career member of the team, who will thrive in a highly collaborative team environment. With the team’s support, you will develop and execute innovative and rigorous scientific approaches, and will effectively communicate findings to both internal and external stakeholders.

Job description

A Day in the Life

At Genomics, we tackle major scientific challenges in harnessing genomic data to improve human health. This role is part of the Life Sciences team, working on a range of challenges spanning the therapeutic development cycle – including discovering and validating therapeutic targets and developing patient stratification strategies. The Scientist is an early-career member of the team, who will thrive in a highly collaborative team environment. With the team’s support, you will develop and execute innovative and rigorous scientific approaches, and will effectively communicate findings to both internal and external stakeholders.

For example, a Scientist may:

Generate novel therapeutic hypothesis for diseases with unmet need, mining the in-house data resources using statistical approaches to understand the causal pathophysiology of disease, identify and triage potential targets with genetic evidence, and build a rich understanding of the cell types, cellular function, tissue-level characteristics, patient populations and biomarkers needed to support a development program.
Apply and optimise risk tools for patient stratification that combine genetic information (through polygenic risk scores) with conventional risk factors, to find those individuals most at risk of disease onset or progression and those most likely to benefit from particular therapies.

To be successful in this role, you will: bring a strong foundation in the application of statistical and computational techniques in biomedical science that you can apply in innovative ways, thrive on analysing 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.

Who You Are

Must have, solid foundations in:

Statistics (modelling, regression)
Genetics and molecular biology
Statistical programming (e.g., Python or R)

Preferred:

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, containerisation)
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

Requirements

Statistics (modelling, regression)
Genetics and molecular biology
Statistical programming (e.g., Python or R)
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, containerisation)
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

Benefits

401(k)
Medical, Dental & Vision
Health Savings Account
Life & AD&D Insurance
Disability Coverage
25 Days Vacation
8 Federal Holidays
Year-End Shutdown
Bank Your Bank Holiday
Hybrid Working
Continuous Learning
Clear Career Path

Company overview

Genomics Ltd is a pioneering transatlantic TechBio company founded in 2014 by four world-leading statistical and human geneticists from the University of Oxford, including Professor Sir Peter Donnelly and Professor Gil McVean. The company specializes in harnessing the power of large-scale genetic information to transform healthcare through predictive genomics. Genomics develops advanced polygenic risk scores and integrated risk tools that enable precise disease prediction for common conditions like cardiovascular disease, diabetes, and various cancers. Their platform combines genetic insights with traditional risk factors to provide personalized health assessments that can predict disease risk well before symptoms appear. The company partners with global pharmaceutical companies, healthcare systems, and research institutions to accelerate drug discovery and improve clinical trial design. With offices in Oxford, Cambridge, London, and North Carolina, Genomics employs over 150 experts including genomic scientists, computational biologists, statisticians, and software engineers. Their innovative approach aims to shift healthcare from reactive treatment to proactive prevention, enabling earlier interventions and more targeted screening strategies.