Associate Director of Phenomics

GSK•Upper Providence Township, PA

About The Position

At GSK, we have bold ambitions for patients, aiming to positively impact the health of 2.5 billion people by the end of the decade. Our R&D focuses on discovering and delivering vaccines and medicines, combining our understanding of the immune system with cutting-edge technology to transform people’s lives. GSK fosters a culture ambitious for patients, accountable for impact, and committed to doing the right thing, making sure that we focus our efforts on accelerating significant assets that meet patients’ needs and have the highest probability of success. We’re uniting science, technology, and talent to get ahead of disease together. Find out more: Our approach to R&D We are seeking a highly skilled and experienced individual to join our team as an Associate Director of Phenomics. The successful candidate will have extensive experience in epidemiology, real-world data (RWD), and biostatistics who is passionate about applying these skills to large, diverse human cohorts with real-world health data linked to genetics and other -omics to support drug discovery and development. We are seeking a thought-leader that is able to apply advanced methods across multiple data sources to deliver novel phenotypes that describe disease severity, disease progression, disease sub-populations, biomarker trajectories, and more advanced health outcomes. They should have not only extensive methodological and analytic excellence, but also strong stakeholder communication, project organization, and experience in leading multidisciplinary teams. The Associate Director will show initiative in identifying and implementing creative solutions and will work with agility to address challenging scientific questions, becoming a thought-leader for innovation and integration of deep, longitudinal EHR data with genetic and genomic data.

Requirements

Advanced degree (PhD or equivalent) in a relevant scientific discipline.
Five or more years of experience in innovation and application of real-world data (RWD) and other observational data from large cohorts to answer diverse scientific questions across therapeutic areas.
Five or more years of experience in performing a range of statistical approaches supporting the analyses of identifying analytic cohorts, defining exposures, and measuring associations against diverse types of exposures and outcomes.
Three or more years of experience of delivering novel and impactful insights from complex EHR data individually, or through leadership of a team.
Three or more years of experience in R programming
Three or more years of experience in Epidemiology and causal inference; demonstrated ability to apply advanced Epidemiology / causal inference to diverse datasets; and communicate methods and findings to a broad set of stakeholders.

Nice To Haves

Hands-on experience integrating RWD with proteomic and/or other genetic & genomic data in biobank-scale cohorts.
Strong leadership, 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.
Ability to identify strengths, limitations, potential confounders and bias from the use of RWD and other observational data; and develop/implement strategies to address these issues.
Proven project leadership / management for small teams with a focus on data quality, delivering results, and communication to diverse stakeholders
Excellent problem-solving and analytical skills to address complex scientific questions.

Responsibilities

Leverage longitudinal, RWD and other observational data derived from large, diverse human cohorts linked to genetics and other -omics to create novel, advanced phenotypes that describe disease severity, disease progression, disease sub-populations, biomarker trajectories, and more advanced health outcomes
Proactively lead the design, delivery, and communication of custom analyses requiring advanced epidemiologic and/or statistical approaches to answer challenging scientific questions required for business development, portfolio decisions, or other GSK-critical requests where the integration of RWD with -omic data is an important component.
Advance novel methods (e.g. AI/ML, mediation, interaction, other prediction, …) that improve our ability to develop novel phenotypes and analyses (as described above).
Identify and communicate 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.