Principal Scientist, Pharmacometrics Support
Location: High Wycombe, United Kingdom
Responsibilities
* Preparing R programming scripts to generate non‑linear mixed effect modelling (NONMEM) analysis input dataset(s) for PK, PK/PD or Exposure‐Response analysis, based on requests from PM leader or modelling leader. During dataset generation, PM support also modifies the variable definition file (PM leader or CPP leader is the main author of this document) which clearly defines each variable within this dataset with any additional information as they see fit. The NONMEM input dataset(s) created could be for interim or final analysis. The source used could be interim (uncleaned) or final SDTM/ADAM datasets or other sources; in some cases extensive data cleaning and complex calculations are needed.
* Generating analysis dataset Non‑Compartmental Analysis (ADNCA) input datasets and associated Data Transfer Agreements and metadata.
* Upon request, QC NONMEM or ADNCA input dataset(s) generated by another PM support colleague. Log which QC script is used, which subjects were checked per study, what other aspects were checked within the dataset, the findings of the QC and the follow‑up actions of those findings in a QC document.
* Generating e‑submission package for NONMEM (or other modelling type) analysis. In general the package includes NONMEM input datasets, NONMEM control file, output parameter files, output table files and other files, in addition to supporting documents such as define and var‑names‑descr files. PM support renames the files provided by the PM leader or CPP leader so they fit the naming convention requirements for the e‑submission package if needed, converts these files into the appropriate formats, and places them into the right folder structure then links them to the define and var‑names‑descr files. PM support works closely with the EPOD team to ensure the e‑submission package has the right structure, accurate formats and is placed in the right assembly server directory.
* Interacting with other departments (including but not limited to Data Management, Clinical & Statistical Programming, bio‑analytical teams, Regulatory) and external vendors to communicate the needs of CPP in data collection, data formatting, data representation and cross‑departmental trainings if needed. Promote better understanding across different departments.
* Piloting the generation of the first draft AI‑Authored data specification.
* Performing pharmacometric analysis QC activities in support of the PM Leader.
* Driving CPP internal process improvements in dataset creation, dataset QC (e.g., a standard QC R script with a checklist), pharmacometric analysis QC procedures and e‑submission package preparation; move forward existing automation ensuring high quality and speed.
* Overseeing internal R package updates and maintenance. Creating required training decks for internal use. Actively participating in working groups related to AI‑assisted code generation.
* Presenting cross‑departmental process improvements within/outside the department.
* Carrying out functional responsibilities in accordance with applicable SOPs and regulatory requirements.
Qualifications / Requirements
Education:
* A minimum of a Bachelor’s degree in Pharmaceutical Sciences, Clinical Pharmacology, Chemical/Biomedical Engineering or a related field with 8+ years of pharmaceutical development experience; or a Master’s degree in Pharmaceutical Sciences, Clinical Pharmacology, Chemical/Biomedical Engineering or a related field with 6+ years of pharmaceutical development experience; or a PhD with 4+ years of pharmaceutical development experience is required.
Experience and Skills
Required:
* Expertise with R programming language and R function creation is required.
* A fundamental understanding of clinical drug development and PK & PK/PD population PK modelling concepts is required.
* Good understanding of regulatory requirements and eCTD is required.
* Good understanding of AI‑driven approaches within data programming is required.
* Data‑handling skills and solid understanding of clinical database structures such as SDTM and ADAM is required.
* Excellent written and oral communication skills with strong attention to detail are required.
* Ability to build and improve working relationships inside and outside the department; ability and willingness to work in a cross‑functional team environment (building strategic working relationships) is required.
* Ability to collaborate in a diverse global environment is required.
* Self‑motivation with the ability to work independently with minimal direction is required.
Preferred
* R Shiny programming experience is preferred.
* Prior knowledge with ADNCA and ADPPK dataset standards is preferred.
* Knowledge of principles of non‑linear mixed effect modelling is preferred.
* Prior experience with pharmacometric analysis QC would be an asset.
* Experience within oncology and immunology is an asset.
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