Scientist

Genomics•Raleigh-Durham-Chapel Hill Area, NC

12d•Hybrid

About The Position

Genomics is a science-led transatlantic TechBio combining large-scale genetic and health data with proprietary analytics to accelerate drug discovery and advance predictive, preventative healthcare. We are united by a single vision to help people live longer, healthier lives, using the power of genomics. Genomics aims to help people live longer, healthier lives in two ways: super-charging drug discovery and development for novel treatments with our AI-enabled advanced genetic analytics platform, and by helping people understand their personal risk of common chronic diseases through polygenic risk scores - giving doctors and health systems the chance to get the right people into the right prevention, screening and treatment programmes at the right time. 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.

Requirements

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

Nice To Haves

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

Responsibilities

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.