Investigator, Phenomics

About the position

The Investigator in Phenomics will play a crucial role in leveraging real-world data (RWD) and observational data to support drug discovery and development. This position focuses on producing novel phenotypes for genome-wide association studies (GWAS) and designing advanced statistical analyses to interpret complex data linked to genetics and other -omics. The role requires collaboration with cross-functional teams and the initiative to guide future analytical processes.

Responsibilities

• Conduct hands-on analyses of real-world data (RWD) and other observational data derived from large, diverse human cohorts linked to genetics and other -omics.
• Perform a range of analyses to derive phenotypes suitable for genome-wide association studies (GWAS) across multiple cohorts, health systems, data sources, and analytic environments.
• Design and implement custom analyses requiring advanced epidemiologic and/or statistical approaches, integrating with genetics and other -omics data.
• Understand the strengths, limitations, confounders, and potential biases for any downstream genetic or other -omic analyses that leverage inputs derived from RWD or other observational data.
• Collaborate with cross-functional teams to support the design and implementation of phenomics projects.
• Evaluate and implement new methodologies and technologies to improve the efficiency and effectiveness of RWD / observational data analyses in biobank data.

Requirements

• Master's with 5+ years experience or PhD in relevant scientific discipline.
• Experience in using real-world data (RWD) and other observational data to identify specific populations of disease or other characteristics of interest in large cohorts.
• Experience in performing a range of statistical approaches (e.g. linear and logistic regression, time-to-event modelling) supporting the analyses of different types of exposures against diverse types of outcomes.
• Proficient in R programming.
• Knowledge of the strengths, limitations, potential confounders and bias from the use of RWD and other observational data.
• Experience of project management with a focus on data quality and delivering results.

Nice-to-haves

• Advanced degree (PhD or equivalent) in a relevant scientific discipline.
• Hands-on experience integrating RWD with genetic or other -omic data in biobank cohorts.
• Initiative to apply expertise and experience to guide the evolution of future analytical and business processes.
• Excellent problem-solving and analytical skills to address complex scientific questions.
• Strong collaboration and partnership skills to work effectively with cross-functional teams.
• Excellent communication and presentation skills to convey findings and recommendations to stakeholders.
• Evidence of strong project management skills.
• Experience of genetic imputation and genetic data quality control pipelines.

Benefits

• Competitive salary
• Annual bonus based on company performance
• Healthcare and wellbeing programmes
• Pension plan membership
• Shares and savings programme
• Hybrid working model through Performance with Choice programme