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Merck KGaA
As a Senior Principal Statistical Programmer, you will have the opportunity to work with advanced technical solutions such as R, Shiny, and SAS, allowing you to lead asset teams and mentor junior staff effectively. In this role, you will contribute to global assets across a variety of therapeutic areas, shaping strategic decisions in statistical programming.Your responsibilities will include leading the trial or asset programming team as the Lead Statistical Programmer, ensuring that asset and trial delivery aligns with established timelines and quality standards. You will perform programming activities at both trial and asset levels, including the development of SDTM and ADaM datasets and the creation of specifications. Additionally, you will develop and validate analytical outputs in accordance with the Statistical Analysis Plan and create datasets for integrated analyses like ISS or ISE. You will also be responsible for executing ad-hoc programming activities based on internal and external requests. Actively contributing to statistical programming initiatives, you will support process improvements and innovation while providing expert advice, guidance, and training to trial and asset teams, fostering the development of your colleagues' skills.Who are you:BSc or MSc (in a numerate discipline preferably in Mathematics, Statistics or Computer Science)Proven success in a Statistical Programming role within clinical development at a pharmaceutical or biotech company, or at a CRO, equivalent to a minimum of 9 years directly relevant experience. Experience in an international environment is a plus.Advanced skills in R and SAS Full familiarity of CDISC SDTM and ADaM standards (including specifications, Define.xml, and reviewers guide) and underlying concepts.Strong understanding of processes related to clinical development programs, Experience in leading e-submission processes is beneficial.Demonstrated ability to manage assets effectively, ensuring timely delivery and quality outcomesAbility to provide solutions for complex programming challenges and evaluate alternatives to identify optimal solutions.
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