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Reference #: 406744 Site Name: USA - Pennsylvania - Upper Providence, UK - Hertfordshire - Stevenage Posted Date: Oct 21 2024

We are seeking a highly skilled and experienced individual to join our Phenomics team as an Investigator in Translational Genetics and Phenomics.

We are looking for a candidate who has experience in epidemiology, real-world data, and biostatistics who is passionate about applying these skills to incredibly large, diverse human cohorts with real-world health data linked to genetics and other -omics to support drug discovery and development.  As an Investigator in Phenomics, you will have experience in leveraging real-world data (RWD) and other observational data derived from large databases to: 1) produce novel phenotypes for genome-wide association studies (GWAS); 2) design and implement custom analyses requiring advanced epidemiologic and/or statistical approaches (e.g. time-to-event modelling, trajectory modelling leveraging clinical laboratory data, clustering or similar approaches to identify patient sub-populations, ...); and, 3) advise stakeholders across the Research organization on the interpretation of results that leverage inputs and/or methods from RWD including limitations, confounders, and potential bias.

You will show initiative in applying your expertise and experience to guide the evolution of future analytical and business processes.

We create a place where people can grow, be their best, be safe, and feel welcome, valued and included. We offer a competitive salary, an annual bonus based on company performance, healthcare and wellbeing programmes, pension plan membership, and shares and savings programme. We embrace modern work practises; our Performance with Choice programme offers a hybrid working model, empowering you to find the optimal balance between remote and in-office work.

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This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following: 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 to gain insights into translational and other questions arising from the Research organization. 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. Why you? Basic Qualifications: We are looking for professionals with these required skills to achieve our goals:

Master's with 5+ Yrs 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 (continuous, binary, count, ...) 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 deli ering results

Preferred Qualifications: If you have the following characteristics, it would be a plus: 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 (including the appropriate generation of figures and tables to visualise results) to convey findings and recommendations to stakeholders. Evidence of strong project management skills. Experience of genetic imputation and genetic data quality control pipelines.

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